Renal DMSA scan morphology analysis using undecimated wavelet transform and isocontours

12 Int. J. Reasoning-based Intelligent Systems, Vol. 5, No. 1, 2013

Copyright © 2013 Inderscience Enterprises Ltd.

Renal DMSA scan morphology analysis using undecimated wavelet transform and isocontours

Ana Gavrovska*, Milorad Paskaš, Vladimir Kovačević and Irini Reljin Department of Telecommunications, Faculty of Electrical Engineering, University of Belgrade, Serbia Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] *Corresponding author

Abstract: Signal and image processing tools open up opportunities to develop modules that can be interesting for supporting clinical decisions. In this paper, the aim is to develop an algorithm for providing better insight into renal scintigraphy scans using sparse transform such as discrete wavelet transform and isocontours. Renal 99mTc-DMSA scintigraphy scans are widely accepted as the most sensitive method for evaluating renal lesions and parenchymal damage like renal scars. An approach for improving DMSA scan analysis and regions of interest is presented from the aspect of visual inspection and relevant morphology information retrieval. For the purpose of testing archived scans are considered.

Keywords: DMSA scan; renal scintigraphy; UTIs; urinary tract infections; lesions; discrete wavelet transform; starlet transform; isocontours; boundaries; enhancement; watersheds.

Reference to this paper should be made as follows: Gavrovska, A., Paskaš, M., Kovačević, V. and Reljin, I. (2013) ‘Renal DMSA scan morphology analysis using undecimated wavelet transform and isocontours’, Int. J. Reasoning-based Intelligent Systems, Vol. 5, No. 1, pp.12–21.

Biographical notes: Ana Gavrovska received the BS and MS degrees in Electrical Engineering from School of Electrical Engineering, University of Belgrade, Serbia in 2007. She is currently pursuing the PhD degree in signal processing. She is PhD stipendiary of the Ministry of Education and Science, Republic of Serbia. Her research interests include signal and image processing, digital telecommunications, digital filters and sparse transforms.

Milorad Paskaš received the BS and MS degrees in Electrical Engineering from School of Electrical Engineering, University of Belgrade, Serbia, where he is currently pursuing the PhD degree in Image Processing. His research interests include signal and image processing, multi-fractal analysis and active contours.

Vladimir Kovačević received Bachelor’s degree in Computer Science in 2007 and Master’s degree in Electronics in 2009 from School of Electrical Engineering, University in Belgrade. He is currently attending PhD studies on the same faculty at the Department of Telecommunications. His area of work includes development of video and image processing algorithms optimal for real-time execution on highly parallelised processor systems.

Irini Reljin is a Professor at the School of Electrical Engineering, University of Belgrade, Serbia. She teaches subjects in the fields of television, multimedia telecommunications and telecommunications systems at basic, master and PhD level. Her research areas include: digital telecommunications, multimedia systems, television, neural networks, optical communications, linear and non-linear methods of signal processing.

This paper is a revised and expanded version of a paper entitled ‘Renal DMSA scan morphology analysis using undecimated wavelet transform and isocontours’ presented at the ‘Jubilee 10th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services (TELSIKS’2011)’, Niš, Serbia, 5–8 October 2011.

Renal DMSA scan morphology analysis 13

1 Introduction

The realisation of medical systems for acquisition, analysis and processing medical signals and images require a large amount of information and data for choosing the relevant and obtaining the proper decisions. Biomedical records, discrete measures and other laboratory test results and/or mobile equipment test results represent only the part of valuable data regarding the physician decision making and diagnosis support. Clinical analysis implies both biomedical and administrative data provided by the final report. Beside laboratory results, cause-effective relationships, disease history, the way of treatment/care, physician(s) and organistion (hospital) data, patient(s) data are only some of the valuable information within the record. Bearing in mind the standards (such as PACS, DICOM), that are often partially integrated and/or possibilities considering the availability of the physicians, their effectiveness, equipment and technology, travel costs, solutions are often cost-effective oriented (Kvedar et al., 1998; Darkins and Cary, 2000).

Analysis of medical images often deals with visual inspection with or without a help of Computer-Aided Systems (CAD). The questions about solving the problems of overlooking certain abnormalities, information retrieval, multimodality and the lack of data have been opened in many fields of research and largely depend on the type of biomedical signals that are investigated. The aspect of telemedicine is also valuable, as well as conditions within the user workstation (laboratory and non-laboratory) presented in Figure 1.

Signal and image processing techniques and CAD seem to be less investigated in nuclear medicine imaging, to be specific scintigraphy, comparing to other modalities (ultrasound, medical radiography, magnetic resonance). The reason for this seems to be the counting principle in soft-copy records, i.e. the records that are visible for the time of

recording on available monitor(s) using the available software. The records are often saved as still/dynamic images (frames) and/or printed.

Urinary Tract Infections (UTIs) represent one of the most common bacterial infections among children. The bacterial growth and inadequate treatment may cause serious kidney damages and have lethal consequences. Possible detected abnormalities – lesions may develop into renal parenchymal damage such as renal scarring, hypertension and even kidney failure. A technique of 99mTc-DMSA scintigraphy is widely recognised and accepted as the most sensitive method for evaluating renal abnormalities and renal scars (Kuzmanovska et al., 2003; Avner et al., 2009).

UTI occur frequently in paediatric patients. Both primary (congenital) and secondary (non-congenital) abnormalities may occur. The cause of UTI represents potential obstructed normal urodynamics, urinary tract dysfunction and bacteria transfer due to the return of urine.

Medical image analysis in the case of a patient with confirmed UTI is necessary and it often includes other techniques such as renal ultrasonography, cistography and/or scintigraphy. In this paper, we investigate the possibility of enhanced visual inspection of scintigraphy scans for better insight and avoiding oversight in the potential lesions. Obtained images may reveal valuable information about morphology of the kidney and possible renal abnormalities. In the cases where standard equipment is not available it may be considered that partial morphology evaluation can be valuable for DMSA scan analysis.

Static renal 99mTc-DMSA (Technetium-99m Dimer captosuccinic acid) scintigraphy is used in nephrology for focal regions extraction of possible renal dysfunction. Scintigraphy can be used as an initial tool for renal abnormality when febrile UTI occurs. Such approach is often used as a confirmation of the previous pathological findings.

Figure 1 Scan/report analysis under laboratory and non-laboratory conditions: aspect of telemedicine and reusing the records (see online version for colours)

Laboratory analysis

Under non-laboratory conditions

Under laboratory conditions

Preparing report

Analysis of the report & Information retrieval

Available data sets &test-beds

14 A. Gavrovska et al.

In approximately 2–4 hours after the initial injection, a combination accumulates in the region of kidneys and planar scintillation gamma camera is used for detection of a weak gamma radiation emitter. Radioactive material is noticeable in scans and dependable on the blood flow through the kidneys and the integration of tubular cells. Obtained scans are called DMSA scans in the rest of the paper.

In the part of data acquisition under laboratory conditions, the children with UTI are tested for possible lesions and nuclear medicine imaging technique of scintigraphy is used. A patient receives an injection with a small amount of radioactive tracer into a vein, usually in the arm. An agent of DMSA is combined with radioactive molecule 99mTc for the purpose of imaging.

In the process of scintigram scanning, accepted photons (counts) in selected Region of Interest (ROI) of appropriate surface/volume (pixel/voxel) are counted for specific amount of time. In this way, it is possible to observe renal damages using scintillation camera and radiopharmaceutical distributions in patients’ organs that indicate renal functions.

Resulted DMSA scans are often archived as a final step of laboratory analysis, with or without the additional discrete measures (like counts versus time in specific region, region boundaries, relative and absolute 99mTc-DMSA uptake measurements) and some administrative data (technique (99mTc-DMSA) indicator, patient name, date, time, position, etc.). The final report may have additional information that was previously explained. Even though, the approach is based on the counting principle, the morphology of DMSA scans are considered as a valuable source of information for visualising the potential problem and for the purpose of the morphology visual inspection.

The paper is organised as follows. Important issues of morphological investigation of a DMSA scan are presented in Section 2. We present an algorithm for marking the scan for the purpose of distinguishing the normal kidneys and the most common morphological abnormalities presented by DMSA scan. Our strategy explained in the paper is based on using the discrete wavelet transform, isocontours and mathematical morphology tools in order to avoid time-consuming approach. Then, in Section 3, the simulation settings are explained with reference to the wavelet transform parameter selection, isocontour setting and scan enhancement. Some of the experimental results are depicted in Section 4 obtaining DMSA scan representation. The initial region segmentation and suspicious region detection results are presented with discussion. Finally, Section 5 is intended for our conclusions and future work.

2 Morphological investigation presented by DMSA scan

Renal evaluation of the parenchymal damage by 99mTc-DMSA planar scintigraphy and SPECT scintigraphy revealed that 99mTc-DMSA planar scintigraphy is still very useful for evaluating gross structural damage (Itoh et al.,

1995). 99mTc-DMSA renal scintigraphy is considered to be valuable method for recognising the renal morphology changes (Kuzmanovska et al., 2003). In the example of detecting renal scars it is considered as significant tool. Morphology of planar DMSA scans may contribute to a decision whether the renal units are normal or abnormal.

Recent work (Landgren et al., 2011) emphasises the need for designing a system for computer-aided diagnosis from 99mTc-DMSA scintigraphy images and diagnosing kidney lesions in children and adolescents. Designing a CAD system is a difficult task. Nevertheless, image analysis may contribute to both image restoration and image retrieval among archived clinical studies.

In DMSA morphological investigation some of the common aspects of qualitative evaluation are listed (Wallin, 1997). Isocontours or isocount levels used in the paper are one of the most often used criteria for visual tracking region widening and narrowing. There are several propositions for boundary detection besides thresholding and isocontours technique. It is often based on delineation, but there are other approaches that may be used such as moment-preserving thresholding (Wu et al., 2006), active shape models (Landgren et al., 2011). There are some of the guidelines for processing the planar images that may be valuable in this case (Tilkemeier and Wackers, 2009).

As multiscale and sparse methods, like wavelet-based approaches, have become very popular in the last couple of decades (Starck et al., 2007; Mallat, 2009; Starck et al., 2010). We decided to implement a discrete wavelet transform for the feature extraction purpose. Further explanation will be given in the rest of the section.

The visual inspection of morphological properties of the scan allows detection of lesions, renal scars and acute changes in renal parenchymal of the kidneys. The support for avoiding oversight may significantly improve DMSA scan morphology analysis.

Activity uptake is of importance and the representation of the kidneys in these scans is used for renal morphology evaluation and searching for possible abnormalities. Those abnormalities are usually manifested as renal scarring, shrinking, areas of reduced accumulation, etc.

Detection of the normal shape, density and other morphological properties presents a problem for itself. One of the most interesting regions for the analysis is candidate lesions. DMSA scan can be characterised by wedge-shaped areas of locally reduced intensity.

Renal scars are very important to detect. They represent renal contour defect with reduced uptake. In the case of renal scarring or general kidney contraction existence, it is possible to diagnose reflux nephropathy, where the kidney is damaged by the backward flow of the urine into the kidney.

The regions where radioactive tracer accumulation is reduced may be considered as another type of morphological abnormality as hypofixation, preserving renal contour. If there is a reasonable doubt that there are one or more regions that show changes of inflammation, the acute focal pyelonephritis may be diagnosed. Hypofixation can be detected in the whole kidney and this also may be considered as another morphological abnormality.


Planar DMSA scans for better visual inspection are usually made in several positions: PA – posterior-anterior, LO – left oblique, RO – right oblique, possibly AP – anterior-posterior position. In this way, it is possible to select the position where abnormalities may be more visible. The reports usually contain different position images for better inspection. Nevertheless, for the purpose of simulation in this paper, archived reports are used so that the best position for visual inspection is selected.

The block diagram of the presented approach is showed in Figure 2. After standard hospital workflow, data acquisition under laboratory conditions is performed and the report that includes the record of DMSA scanning is prepared. Database for testing purposes is consisted of images that show the area around kidneys. Those images are without high intensities due to the existence of bladder in the obtained image (possible additional ROI crop).

The following steps will be specifically explained:

Standard setting the isocontours of kidneys;

Setting the boundaries of kidneys for distinguishing the region of kidneys and background pixels;

Image enhancement for visual distinguishing of bumps and dents and;

Final isocontour setting.

Finally, for the purpose of initial examination, sub-regions of the kidneys are examined, as well as the possibility of suspicious region detection.

Figure 2 Block diagram of the approach (see online version for colours)

Data acquisition Results

Initial isocontours setting Final isocontour setting

Suspecious region detection

Region segmentation

Preparing report /record

Possible additional ROI crop

Kidney area segmentation -Boundaries Image enhancement

2.1 Discrete wavelet transform

The rapid development of wavelet theory enabled widening applications based on multi-resolution. Wavelets and level sets are used here for relevant region detection as well as for boundary detection. By applying multiscale approach, it is also possible to provide enhancement of the particular areas of locally reduced intensity.

The particular selection of wavelet transform (WT) and its fundamental settings still need to be explored. For the simulation purposes, we used one of the most applied WT.

Redundant WT and in particular Undecimated Wavelet Transform (UWT) is considered to be one of realisations that are very useful in restoration and analysis of data (Starck et al., 2007; Starck et al., 2010). This is mainly due to the translation-invariance property. À trous algorithm (algorithme à trous) is famous for an efficient way for UWT implementation. Standard discrete WT (DWT) likely leads to noticeable artefacts.

We applied so called Isotropic Undecimated Wavelet Transform (IUWT) or starlet transform (ST) for the decomposition purposes. It is famous for the astronomical domain applications and enhancement results (Starck et al., 2010). Cubic spline (B3-spline) is shown to be satisfying choice for its simplified implementation. The use of this spline leads to convolution with 5 5 2D FIR. Wavelet coefficients are obtained by taking the difference between two resolutions, resulting in set {w1,…,wJ, cJ}, where wi (i = 1,…,J) represent wavelet (detail) coefficients and cJ the final approximation result. The reconstruction is simply obtained by summing all the wavelet scales and the final smooth sub-band.

Superimposition of resolution scale information on the original image may lead to edge and generally image enhancement that may help visual inspection and better segmentation results.

3 Simulation

Database that is used in the simulation is consisted of archived Tc99m-DMSA scintigrams, where scintigraphy is performed in 20 children and adolescents. DMSA scans in this database show both normal and abnormal renal function. Besides normal kidneys, morphological abnormalities that occur are:

Renal failure (the absence of visualistion);

Partial hypofixation with preserved and not-preserved contour (defects);

General kidney hypofixation;

Global contraction and shrunken kidney with significant kidney function reduction;

Vulnerable or defected poles/scars (different number and position – upper, middle, lower pole)

for both left and/or right kidney, etc. We only want to point out the morphological variety in cases that are used in the simulation. This is not a proposed classification.

Input images have different baseline characteristics regarding: the position while scanning, the quality of the record/input image, image size (due to image crop), etc. All of the input images are greyscale.

The local accumulation of the tracer is proportional to the density of functional cells. In visual inspection one of the main assumptions is that the distribution of counts is proportional to distribution of intensity values.


3.1 Isocontours and boundaries selection

Using isocontours can be useful for tracking irregularities of the presentation of the kidney. Nevertheless, boundary detection is crucial for distinguishing the background pixels and kidney regions.

All the pixels that have specific constant value may be considered as a level set. In this paper, contours represent perimeters of such sets, prepared using mathematical morphology tools (Gonzalez et al., 2004; Gonzalez and Woods, 2008). To be precise, morphological closing and opening using small, flat, structural diamond-shaped structuring element are applied, after filling region holes.

As the initial step, the isocontours are set in a standard way for 20%, 40%, 60% and 80% of the image maximal intensity (Wallin, 1997).

The appearance of such isocontours depends on the selected scale. It is considered that after applying twice the 9 9 average filter, as in mentioned by Wallin (1997), isocontours are reasonably detected in most of the cases. In Figure 3c and 3d, fixed isocontours are depicted for an example of healthy and unhealthy renal function, respectively, where in the second case renal defect is set in the middle area of the left kidney.

Figure3 DMSA scan example for (a) healthy and (b) unhealty renal function – renal defect is set in the middle area of the left kidney. Fixed isocontours are set (c) and (d) after applying average filter and (e) and (f) using approximation image obtained by wavelet transform (see online version for colours)

The similar results presented in Figure 3e and 3f that obtained using WT, as explained in Section 2.1 for the appropriate level of decomposition (reference level). For the example showed in Figure 3b, 3d and 3f the level set presented by green pixels should denote boundaries of ROI. Nevertheless, the fixed choice may give unsatisfying result as in this case.

The approach that was applied here for obtaining satisfactory results for the used test-bed consists of several steps. The desired output is one or two regions of interest dedicated to kidney regions.

Two levels are selected: one for decomposition and enhancement purposes (decomposition level) and one for setting the boundary of kidneys and isocontours (reference level).

After obtaining approximation image from the reference level

1 we choose one or two regions that cover most of the pixels for the threshold (initially 20% of maximal intensity);

2 if detected second smaller object presents an irrelevant small segment, it is deleted and the number of detected objects is calculated again;

3 in the case that there are two connected regions detected boundaries are found by isocontour representation (green pixels), otherwise the threshold is increased and

4 steps 1–3 are repeated, until threshold is 40% of maximal intensity or the number of connected regions is exactly two.

In step 1 we use four-connectivity to retrieve the result from the fixed threshold that correspond to green isocontour and repair the information by rejection of the irrelevant small segments in step 2. Only in the case we have one large connected region detected, there is a reasonable doubt that it belongs to eventually two regions of interest. In that case, the threshold is increased by fixed value (1%) in order to separate these regions. The number of connected regions is again calculated using eight-connectivity criterion.

In Figure 4, several DMSA scans of different relevant morphological characteristics are presented: (a) two normal kidneys, (b) the absence of the right kidney, (c) shrinking of the right kidney, (d) abnormality detected in the middle part (scarring) of left kidney, (e) abnormalities detected in the upper, lower and middle part of the left kidney, (f) abnormalities detected in the upper poles of both kidneys. In Figure 4g–4l detected boundaries of relevant regions are depicted, respectively.

Figure 4 (a)–(f) DMSA scans of different relevant morphological characteristics and detected boundaries of relevant regions, respectively, presented in (g)–(l)

The reference level of decomposition (the level used for boundary detection) is selected as round(1.5 l), where l = min(round(log2M)), round(log2N) and the size of used scan is M N.


3.2 Scan enhancement

The enhancement of DMSA scans may contribute to better visual inspection. Human visual system is able to distinguish regions that represent more or less accumulation of the isotope. It is believed that if we enhance the edges between those regions the results of visual inspection may be quite improved.

ST is used here for generating different scales of the original image. Superposition of selected obtained results (approximation image from the last decomposition level and detail images) may be used for image enhancement for numerous applications (Starck et al., 2010). The similar approach is used in the paper.

The level of decomposition selected for this purpose is round(3l), where l = min(round(log2M)), round(log2N) and the size of used DMSA scan is M N. Unlike standard DWT, the ST often requires higher decomposition levels due to decreasing level of unnecessary edge presence.

As noted before, reduced intensity areas in the region of kidney may be very important. This is the reason that even if they are not obviously visible, the morphology needs to be enhanced. This is justified by the fact that possible parenchymal damages may become easier to detect. The enhancement is performed so that detail coefficients from the last two decomposition levels are picked and added to selected parameter ka times original input image. In other words, every result of decomposition is multiplied by ka except detail coefficients from the last two levels that are multiplied by 1 + ka. In Figure 5a, the enhanced original scan is presented for ka = 0.005 as an example.

Figure 5 (a) Original image and (b) original image enhancement. Enhancement result for ROIs (c) without and (d) with initial levels (ka = 0.005)

After detection of ROIs (kidney boundary detection), the enhancement is calculated as a product of reference level approximation image and edge-enhanced image. The result is depicted in Figure 5d. The simplified visual inspection, without detail coefficients from initial levels preserving only coefficients from two last levels, is presented in Figure 5c.

By changing the parameter ka, the visual inspection of dents may be improved. In Figure 6b–6f results of scan enhancement for ka = 0.1, 0.05, 0.01, 0.005, 0.001 are presented, respectively.

Figure 6 (a) Original kidney area and result for scan enhancement parameter: (b) ka = 0.1, (c) ka = 0.05, (d) ka = 0.01 (e) ka = 0.005, (f) ka = 0.001

3.3 Setting isocontours

After performing boundary detection new isocontours are set in the case where boundaries are different from initially set ‘green’ pixels (20% of maximal intensity).

The new ‘green’ isocontours are boundaries detected as explained in Section 3.1. Other three isocontours (marked as yellow, blue and red) are equally placed between the threshold value that corresponds to boundaries and maximal intensity. The reference level of decomposition and appropriate approximation image is also used in this case.

Initially set isocontours are presented in Figure 7a. The corrected isocontours superimposed on the original scan are shown in Figure 7b.

Figure 7 (a) Initially placed isocontours and (b) corrected isocontours superimposed on the original scan (see online version for colours)

These isocontours may serve as controlling lines for making decisions and shape preservation. Some of the experiments are conducted using this proposal.

4 Experimental results

The goal of simulation conducted in the paper is to present a possibility for tool selection whose purpose will not be to replace physicians, but to enable them image analysis support for pointing out relevant regions. Some of the tools are presented in Section 3.


In order to evaluate results, both visual inspection and region segmentation are used. Most of the algorithms for segmentation of objects/regions from the scene are based on one of two characteristics: intensity and discontinuity (Gonzalez et al., 2004; Gonzalez and Woods, 2008). Both approaches are used for evaluation.

Low-level intensity regions are of importance. In this paper, we use simple thresholds and mathematical morphology approach for obtaining relevant regions inside kidney regions.

Image segmentation based on discontinuity represents image partitioning based on abrupt changes in the intensity and similarity. Edges may be considered as one of the most important low-level characteristics for the segmentation purpose and watershed segmentation is only one method that can be based on edges (Vincet and Soille, 1991; Salman, 2006). The enhancement image as presented in Section 3.2 is used for obtaining watersheds.

One of the main questions to be answered is related to which pixel belongs to which class (pixel-based approach) or which region belongs to which class (region-based approach). Isocontours are used for controlling purposes.

4.1 Region segmentation and suspicious region detection

So far it is showed that the simple enhancement may improve the result of visual inspection. The human visual system is capable to distinguish the regions from the scene and to detect some of the details. This is also the case in visual inspection of DMSA scans. In Figure 8, the examples of (a) hypofixation in the upper pole areas of both kidneys, (b) both hypofixation and focal defects in upper, middle and lower pole area, (c) defect in the middle pole area become more visible in the enhancement results presented in Figure 8b, 8d, and 8f, as well as other morphology characteristic accumulation areas within boundaries that may be interesting for evaluation.

Figure 8 (a), (c), and (e) Original DMSA scans and appropriate enhancement results presented in (b), (d), and (f), respectively (see online version for colours)

In order to additionally evaluate the capability of detecting dents, initial testing considering obtained results are performed. The common approaches of intensity- and discontinuity-based segmentation for the goal of suspicious regions detection are applied.

Setting thresholds and identifying low accumulation areas may be considered as standard steps in scan morphology analysis. By using low thresholds and mathematical morphology tools in order to eventually select areas of reduced distribution, in Figure 9 the comparison of enhanced scan result and original scan is presented.

Figure 9 Setting low threshold value for low intensity accumulation detection in (a) enhanced and (b) original right kidney region and (c) enhanced and (d) original left kidney region (see online version for colours)

Within the boundaries, the significant areas of reduced distribution are treated in two steps. In the region outside the yellow isocontours, mathematical closing with the help of 3 3 squares and opening with eight-radius disk structuring elements are used after applying low threshold (10% of maximal intensity). In the right kidney, the isocontour modification is detected as well as the hypofixation in the upper pole area (Figure 9a and 9b). Here, the comparison between the enhanced and original DMSA scan is presented, obtaining similar results. The second step is based on the similar approach of applying selected low threshold and the same mathematical morphology filtering within the yellow thresholds. The comparison of enhanced and original DMSA scan is presented in Figure 9c–9d where the result of increased sensitivity is expected in the enhanced case. The hypofixation in the upper pole should be detected in the left kidney as shown in Figure 9c. The analysis of the original scan failed in this case with the help of existing original irrelevant details. The enhancement parameter of ka = 0.005 is used in this section.

The suspicious regions may be differentiated by the fact to which area of isocontour they belong. Even though the isocontours are important, potential unification of regions (or not taking a region into account, based on the size) may be considered. It is desirable to carry out a decision making and classification step in order to select or reduce and improve presentation of possible defects.


An attempt to obtain region-based presentation of the scan, watersheds as a morphological approach may be used. The watershed transform applies the idea of catchments basins and ridge lines that divides areas drained by different river systems (Vincet and Soille, 1991; Belaid and Mourou, 2009) and is often used with the multi-resolution aspect (Kim and Kim, 2003). These ridge lines represent watershed pixels and they are equally likely to collect uniform ‘rain falling’ in either of the neighbouring catchments basins.

For the purpose of region-based representation, the watershed segmentation using gradients is applied.

From the multi-resolution aspect the initial levels should be avoided for the reason of oversegmentation. There is also a risk of undersegmentation in the case of applying watershed segmentation on the simple approximation image result. The regions where undersegmentation is obvious are found around boundaries.

If the selection of approximation image is appropriate (as for reference level of decomposition) and analysis is performed within the boundaries, the DMSA scan presentation using watersheds can be viewed in Figure 10a and 10b. Here, the region (pixon) presentation is consisted of 124 pixons (Yang and Jiang, 2003). Nevertheless, if we add isocontours it is believed that by the cost of increasing the number of pixons, this may serve to shape preservation.

Figure 10 (a)–(b) The watershed segmentation performed on approximation image. The number of pixons is increased from (c) 124 to (d) 224 (see online version for colours)

The isocontours may be used for controlling the morphology by adding additional watershed pixels where this is needed and Figure 10c shows these white watershed pixels (isocontours). The new watershed scan representation with 224 pixons is presented in Figure 10d. The increase of the number of pixons in watershed presentation of the scan before and after adding new watershed pixels is about 80%.

The similar approach may be performed in the enhancement scan result without irrelevant details originated from initial levels of decomposition, as shown in Figure 5c. The watershed segmentation applied using this enhancement DMSA scan result generates more pixons. One may notice additional small pixons in the positions where edges are situated and that may be relevant for obtaining the better result in classification. In this case, the number of pixons (252) is increased by inserting watershed isocontours pixels obtaining 368 regions (the increase of 46%). The results are presented in Figure 11.

Figure 11 (a)–(b) The watershed segmentation performed on enhanced DMSA scan without initial level details. The number of pixons is increased from (c) 252 to (d) 368. Watershed segmentation of the whole enhanced scan with (e) 1052 and (f) 1235 pixons (see online version for colours)

In this way, not only the region of kidneys can be evaluated, but also the background area. The differentiation of the activity within and around kidneys may give valuable information. The number of regions is not too big for this kind of images and presented approach (1052 and 1235, as shown in Figure 11e and 11f, respectively). This means that the approach may be time-effective.

For the testing purposes, three fixed thresholds within the boundaries are set and watershed segmentation results from Figure 11b and Figure 11d are used. The comparison between applying watershed segmentation with and without isocontours is presented in Figure 12. The use of isocontours shows results with better shape performance in classifying pixons into three classes based only on the median values of each pixon.


It should also be noted that in classification using watersheds the more natural result is obtained by classification the watershed pixels after performing pixon classification than with the selected pixon. The watershed pixels are ‘classified’ here in the sense of intensity closeness to neighbouring pixons (regions).

Figure 12 Setting three classes (a) with the use and (b) without the use of isocontours (see online version for colours)

The ST applied on the scan X obtaining a set of images/ coefficients {w1,…,wJ, cJ}, that correspond to detail images wi (i = 1,…,J), and approximation image cJ is easy to treat and reconstruct by simple summation

1

( , ) ( , ) ( , ) ,J

J ii

X k l c k l w k l

(1)

and the simple enhancement image R is obtained as

1( , ) ( , ) ( , ) 1 ( , ) ( , ) ,ref a J a J JR k l c k l k c k l k w k l w k l (2)

where cref denotes the approximation image from the reference level. Even with the simple settings and classification methods it may be possible to distinguish morphology specific regions in enhanced images.

The use of isocontours showed as valuable approach for improving scan representation. Only a median value of pixons is considered here, obtaining obvious improvement in region differentiation than with the original scan. Additional valuable information of lesions and kidneys (Landgren et al., 2011) need to be included. The future work should be oriented to specifying the parameters and additional conditions in DMSA scan treatment.

5 Conclusion and future work

In this paper, an approach and initial testing based on IUWT(ST) and isocontours is presented for faster and efficient visual inspection, as well as the possibility of information retrieval from the archived DMSA scans. One can conclude that even archived records and reports of DMSA scans may give satisfying results in detection of regions of interest.

The approach gave initially satisfying results in revealing and gathering relevant regions that are not quite observable. This may be considered useful in procedures of scan restoration and retrieval.

Our current efforts is oriented towards tool selection, exploitation of sparse transform approaches and other signal and image techniques that can be useful for this type of images. Some of the results are presented here. We do not take into account additional information contained in the report, which might be useful for including into decision making support.

Future work will focus on making further conclusions about defects and morphology changes, as well as other practical (semi-) automatic evaluations applying multi-resolution concepts.

Future possibilities for investigation represent improving obtained results, database and test-beds expansion, improving the quality of overall analysis, diagnosis and treatment of UTI, providing scan comparison, efficient search among case studies, etc. This not only concerns efficient hospital workflow, but also obtaining better results in management and communication in relation physician–physician and physician–patient/contact person.

The final goal should be directed towards developing efficient CAD systems and telemedicine systems and their increased efficiency. The reason for this can be found in the ratio of the number of patients that have UTI to number of available physicians.

Acknowledgements

The first author would like to thank the Ministry of Education and Science, Republic of Serbia, for financial support.

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Renal DMSA scan morphology analysis using undecimated wavelet transform and isocontours

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