FARO et al.: BASAL GANGLIA ACTIVITY MEASUREMENT BY AUTOMATIC 3-D STRIATUM SEGMENTATION IN SPECT IMAGES 3271

Fig. 3. Manual Striatum’s Activity Evaluation performed by nuclear medicinephysicians: (a) The 12 slices of the transaxial plane and (b) Visual SlicesSelection.

applications and the existing 3-D methods striatum’s activityassessment using SPECT images.

III. LITERATURE REVIEW

Nuclear medical imaging, such as PET, SPECT, and Mag-netic Resonance Imaging (MRI), offers a 2-D representationof 3-D human body regions using study data sets, commonlyavailable in DICOM format as single or multi-frame images.Fast and accurate 3-D reconstruction of bioimaging structuresfrom 2-D slices is a challenging problem, involving the de-finition of objects’ boundaries in an image and their spatialreconstruction to obtain a volume or a surface. Nowadays, thisrepresents a widely explored field of bioimaging research, stillin great expansion.

Generally, 3-D medical reconstruction involves three steps:image enhancement, image segmentation, and volumetric dataset reconstruction. Image enhancement, usually, consists ofdenoising, sharpening, or blurring filters, emphasizing imageedges for the subsequent steps. Image segmentation, instead,refers to the process of partitioning the image into multiple seg-ments, representing different objects and boundaries. Finally,3-D volume rendering is performed to obtain a 3-D representa-tion of the interested biological structures, for a better analysis.

Several approaches for automatic medical image enhance-ment have been proposed, e.g., [9]–[11], most of them stronglydepending on the image registration phase.

Similarly, many automatic 2-D image segmentation meth-ods have been developed and can be classified in three cate-gories: region growing, edge detection, and clustering methods[12]–[14]. Typically, edge detection algorithms are the onesmost used in medical applications, e.g., [15]–[17], to the extentthat they can be adopted as the basic step of image segmentationfor medical applications. Many of these approaches are targetedto brain image segmentation, e.g., in [18]–[22] in different typesof medical images (MRI, PET, SPECT, X-Ray) and use edgedetection and thresholding of greyscale images.

After image segmentation, size filtering methods are usedto eliminate small objects contours before 3-D volumereconstruction.

Two basic approaches, surface rendering and volume ren-dering, have been extensively applied to medical data sets for

3-D brain’s structures visualization and are mainly based on twomodels [23]: ray casting and marching cubes.

Volume ray casting is one of the most used volume renderingtechniques, which permits to obtain high quality 3-D objectsrepresentations but at the price of a complex and computer-intensive runtime process. The main aim of volume renderingapproaches is to give a feel of the different densities of objectsdata which affect light, mapping voxels to colors and opaci-ties. Examples of 3-D brain segmentation algorithms based onray casting are the ones proposed by Jiang et al. [24] or byKainz et al. [25].

The marching cubes method is also a commonly used tech-nique for performing surface rendering. The basic principlebehind this approach is to subdivide space into a series of smallcubes (cells). The iterative evolution of this algorithm consistsof a replacement of cubes corners with an appropriate set ofpolygons, to obtain a polygonal surface which approximates theoriginal data set. The marching cubes algorithm is based on theextraction of an isosurface, starting from an isovalue that usu-ally is a grey level threshold. The quality of the marching cubesreconstructed object, essentially depends on the number ofinitial cubes along each axis, called sample density. Examplesof 3-D brain segmentation algorithms based on marching cubesare the ones proposed by Liu in [26] or by del Fresno in [27].

A different approach with respect to ray casting and march-ing cubes is [28] where an automated method for segmentingMRI head images into brain and non-brain is proposed. Thealgorithm - called Brain Extraction Tool or BET - uses aniterative deformable model approach which evolves to fit thebrain surface. A prior semi-global knowledge of the image is re-quired to properly configure the algorithm’s initial parameters.The evolution is made on the basis of two main constraints:the required surface smoothness and the correct fitting of theinterested part of the image. The fitting is usually obtainedthrough an iterative deformation of the growing surface fromits starting point, until an optimal solution is found.

Among these three methods, the one that guarantees thebest performance both in terms of quality of 3-D surfacereconstruction and in terms of processing time is the marchingcubes method [23]. The main issue to solve while operatingwith the marching cubes method is the detection of a correctisovalue for each biological structure. Most of the marchingcubes rendering methods determine experimentally the exactisovalue for a surface rendering. This approach requires a directoperator intervention to understand what is the best value on thebasis of the rendering output.

So far, very few approaches have been proposed for quanti-tative assessment of the dopaminergic system by estimating thestriatum activity using 3-D segmentation techniques with PET,SPECT, and MRI images. These approaches use the completevolumetric data set for the radioactivity measurement and, sincethey reflect the 3-D complexity of the whole structure, a goodaccuracy is achieved.

Farinha et al. [29] proposes an automatic technique forstriatal brain segmentation from high resolution PET images.The extraction of right and left striatum from the background isperformed extracting the brain surface by a deformable surfacemodel, segmenting the obtained surface into two hemispheres

3272 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 60, NO. 10, OCTOBER 2011

and finally extracting the interesting structures from each hemi-sphere. A K-means clustering algorithm is then adopted - usingvoxel coordinates and intensities as features - to identify left andright striatum. Koole et al. [30] developed an MRI-guided seg-mentation technique consisting of: 1) a region growing segmen-tation step to extract the structural striatum using MRI data set,2) a mapping step where the detected regions in the MRI dataset are mapped on the original SPECT data set for the computa-tion of their corresponding activity concentration. Despite thisapproach provides a good accuracy, it has the disadvantage ofrequiring an MRI scan for each SPECT study, which cannot bealways easily obtained in nuclear medicine practices. Recently,Calvini et al. [31] implemented an automatic brain striatum seg-mentation method using a high-definition 3-D template derivedfrom Tailarach’s atlas [32]. The method locates the computedtemplate on the input image by solving an optimization problemand then performs uptake quantification on the striatum, i.e.,the measurement of the radioactivity emitted by the striatum.David et al. [33] studied the striatal activity in patients affectedboth by Alzheimer and by Lewy body disease performingquantitative measurements obtained by a 3-D segmentationmethod. The most recent approach, proposed by Mirzaei et al.[34], uses two multivariate Gaussian distributions estimatedwith expectation maximization algorithm for automatic 3-DVOI (Volume of Interest) extraction.

The main shortcoming of the existing methodologies for theanalysis of brain striatum images in 3-D is that they require adirect intervention by the operator, at least in the starting phase.This affects negatively the final measurements, as stated in [35],where the authors estimated that the error of current methods inmeasuring volumes of interest (VOI) of small structures such asthe striatum is in the range [−7.2%–8.4%].

IV. THREE-DIMENSIONAL STRIATUM SEGMENTATION

FOR QUANTITATIVE ASSESSMENT OF

BASAL GANGLIA ACTIVITY

In this paper, we propose a fully automatic method forquantitative measurement of striatal activity integrating 2-Dimage processing techniques for striatum identification in each2-D slice with a 3-D striatum reconstruction method carried outusing an extended marching cubes algorithm where the isovaluerepresenting the striatum surface is automatically detected. Indetail, the proposed method is so structured: after the transaxialplane extraction from the raw data, the brain area is extractedby means of Active Contour Models [36]. Then a 2-D imageprocessing algorithm processes each slice to automatically ex-tract the striatum. Once the striatum is extracted, an automaticmethod identifies a set of isovalue, which potentially describethe striatum, to be passed to the 3-D striatum rendering method.Finally, measurements of the striatum and background activitiesare taken on the 3-D model. The flowchart of the proposedmethod is shown in Fig. 4.

A. Automatic Brain Area Extraction

In the current practice, nuclear medicine physicians ex-tract the brain area by removing all the information (grabbed

Fig. 4. Flowchart of the proposed method.

Fig. 5. Output of the brain area extraction method: (a) Original Imagewith Noise Outside Brain Area, (b) Thresholded Image, (c) Snakes after10 iterations, and (d) Extracted Brain Area.

gamma rays) outside the brain area by using frequency filters[37], such as Butterworth filters. Since these methods are notdiscriminative, they produce a distortion also in the valueswithin the brain area. For this reason, other approaches havebeen explored in the last years, such as the one proposed byWang in [38] that performs contour extraction in SPECT lungscans by combining deformable contours and image processingtechniques. Accordingly, for automatic brain area delimitation,we use Active Contour Models (ACM) [36], [39], [40] (alsocalled “Snakes”) that are deformable models that “move” imagecontours by following a criterium of minimum energy. Since thesnake is a low-level mechanism that seeks local minima, it isnecessary to position the initial snake in proximity of the brainarea. To accomplish that, a histogram equalization followed bya thresholding phase, performed using the Otsu’s method [41],is applied to the original image [see Fig. 5(a)]. Then the initialsnake is automatically positioned in the rectangle containing theobject previously extracted [Fig. 5(b)], thus avoiding attractiontoward undesired zones of the image. The final brain contouris then extracted by iterating the method (i.e., by moving thecurve, for example in Fig. 5(c) the snake after 10 iterations isshown) until the curve fits the brain contour. Fig. 5(d) shows theoutput of the proposed method.

Finally, a comparison between our approach and the well-established (as indicated in [37]) Butterworth filter applied as apre-processing step on images reconstructed is carried in termsof mean absolute error (MAE) that indicates how much the

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Fig. 6. Flowchart of the 2-D Striatum Extraction algorithm.

details of an image are smeared after a filtering phase and isdefined as:

MAE =1

MN

∑

i,j

(ri,j − xi,j)2. (4)

where ri,j and xi,j denote the pixel values of the processedimage and the original image, respectively. M and N indicatethe size of the original image. With our approach, we obtaineda MAE of 0.17, whereas using the Butterworth’s approach,the obtained MAE is 1.81. This means that our approachremoves the information outside the brain area but preservesthe information inside. The Butterworth’s approach removesalso the non-significant information outside the brain area, but itaffects also the pixels inside the brain area, as indicated by theMAE ratio about 20 times greater than the one obtained withour approach based on ACM.

B. 2-D Striatum Extraction

The 3-D reconstruction method requires an isovalue to buildthe isosurface which best fits the morphological structure of thestriatum. Since the isovalue can be seen as a grey level thresholdrepresentative of the striatum, we must, first, extract it. The flowdiagram of the algorithm for the striatum extraction, from a 2-Dslice, is shown in Fig. 6.

The original raw data [see Fig. 7(a)] is provided as a 16-bitgrey level map. We first apply a colormap to make the imagemore easily processable [Fig. 7(b)]. Then, the image with thecolor map applied is converted into the HSV (hue, saturation,and value) plane. The Hue plane is further extracted [Fig. 7(c)]and a Canny edge detection filter is applied to enhance thestriatum’s edges [Fig. 7(d)]. In the last step, an image fillingalgorithm and a size filtering is applied, thus obtaining theimage shown in Fig. 7(e) and (f). This algorithm is performedfor each slice of the original SPECT data set and provides asoutput the shape of the striatum and the subset of slices whereit is present.

Fig. 7. Output of the 2-D striatum extraction algorithm: (a) Original Im-age, (b) Image with Colormap applied, (c) Hue Plane, (d) Striatum’s Edges,(e) Filled Image, and (f) Output Image.

Fig. 8. Flowchart of the automatic isovalue detection algorithm.

C. Automatic Isovalue Detection

The previous method allows us to identify the subset ofslices where the striatum is visible. In this section, we describethe algorithm for the automatic detection of the isovalue thatis further used by the marching cubes algorithm for the 3-Dstriatum segmentation. The flowchart of the method is shownin Fig. 8. In detail, as described above, an isovalue to build anisosurface can be seen as a grey level threshold.

Our methodology starts from the indexes of the slices, wherethe striatum is present, identified by the previous step. For eachindex, we threshold the corresponding slice of the original dataset with thresholds (k) varying from 0 to the maximum value ofthe grey levels (65535). For each threshold k, we compute thesum of the areas (Area A) of the obtained objects; when thisvalue is equal to the sum of the areas of the objects identified bythe 2-D Striatum extraction module (AreaStriatum AS) in thesame slice, the threshold k is stored as a candidate isovalue to beused for the 3-D reconstruction. The output of this module is aset of candidates isovalue for the further 3-D striatum rendering.Of course, the procedure might not return any value whenimposing the strict condition Area(A) = AreaStriatum(AS),

3274 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 60, NO. 10, OCTOBER 2011

TABLE ITOLERANCE RANGES FOR AUTOMATIC ISOVALUE DETECTION

therefore, it is necessary to introduce a tolerance range to ensurethat for all the images, at least one isovalue is obtained. To finda suitable range, stepwise increases in the tolerance were testedon a data set of 128 123I FP-CIT SPECT images. Table I showsthe achieved results in terms of number of SPECT images (N)over the whole available set for which it was possible to detectat least one isovalue for a given tolerance range. By introducinga tolerance range of ±7%, we were able to detect candidate iso-value for the whole set of images. Selection of the isovalue thatbest describes the striatum’s surface is performed by comparingthe resulting 3-D models for each isovalue from the candidatesset with a reference model computed on a phantom.

D. 3-D Striatum Reconstruction

The proposed approach for 3-D striatum reconstruction ex-tends the marching cubes algorithm by 1) overcoming thelimitation of manual isovalue’s selection and 2) automaticallycomputing the volume of the striatum. In detail, after theautomatic isovalue detection algorithm, our system extracts the3-D striatum surface for the input data set using the detectedisovalue and then estimates the striatum volume. In detail, foreach detected isovalue, we render the striatum volume, and wechoose as the best isovalue the one that identifies a volumewhose morphology is the closest to the reference model com-puted on a phantom (see Section IV). Once the best isovalue hasbeen computed, the surface rendering of the processed SPECTimages together with a model which describes normal (healthy)striatum is performed. Let us note that, although more isovaluesare generally identified for a specific data set (three in average,as shown in Table I), the processing time for the volume’scomputation and surface rendering of the striatum is negligible.

The proposed algorithm uses the Visualization Toolkit(VTK)1 and the Insight Segmentation and Registration Toolkit(ITK)2 framework libraries for surface rendering. Fig. 9 showsthe VTK pipeline adopted for the rendering process. ThevtkImageChangeInformation class is used for centering bothpatient’s and normal’s data sets into the rendering scene. Themarching cubes algorithm is implemented by the vtkContour-Filter class whose output is a polygonal mesh approximatingthe striatum surface. After normals computation performedthrough the vtkPolyDataNormals class, the striatum volume isestimated by the vtkMassProperties object and the final stepsof the rendering pipeline can be performed. The output sceneconsists of two distinct renderers sharing a single renderingwindow. The first renderer deals with the patient’s striatum andnormal striatum 3-D models, allowing independent user inter-

1http://www.vtk.org2http://www.itk.org/

Fig. 9. VTK application pipeline and classes relationships.

Fig. 10. Three-dimensional Surface Rendering in four cases: (a) NormalStriatum, (b) Right Striatum smaller than Left Striatum, (c) Left Striatumsmaller than Right Striatum, and (d) Striatum smaller than a Normal Striatum.

action with each model. Two interaction styles are provided bythe rendering window to easily move the two surfaces along thescene, supporting rotating, panning, and dragging actions for abetter comparison. The second renderer plots the statistical andmeasurement information on the screen.

Fig. 10 shows the 3-D reconstruction in 4 different cases:normal, left striatum greater than right striatum, right striatumgreater than left striatum and both striatum smaller than thenormal case.

After the 3-D model creation, to perform the quantitativeevaluation of the striatum activity, we measure the number ofcounts produced by both the striatum and by the background.

E. Measurements of the Striatum Activity

The measurements of the striatum activity are obtained interms of counts (as defined in Section II) within the identified

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Fig. 11. Occipital ROI Identification for Background Activity Estimation:(a) Slice with striatum and the computed ellipse superimposed, (b) BackgroundMask, and (c) Background Area.

3-D VOI (volume of interest) and are computed on the 2-Dslices. The 2-D striatum extraction module provides as outputthe shape of the two sides of the striatum. The total number ofcounts for each structure (left and right) of the striatum can bedefined as:

CStriatum =slicesup∑

k=sliceinf

∑

i,j∈ROIST RIAT UM

v(i, j) (5)

where sliceinf and slicesup represent the indexes of the sliceswhere the striatum is present, whereas ROISTRIATUM is theROI describing the striatum’s shape in the kth slice and v(i, j)is the grey level at the position (i, j).

The estimation of the background activity is performed bymeans of an occipital ROI which is automatically positionedby our system. The occipital ROI is generated as follows: foreach slice where the striatum is identified, an ellipse inscribedin the brain region is computed [see Fig. 11(a)]. Then, startingfrom the mean point (called P ) of the centroids of the twostriatum (left and right), we extract an arch of the ellipse locatedat the bottom of the brain as shown in Fig. 11(b). If we indicatethe coordinates of the mean point P with (xmean, ymean),and with Hellipse the length of the ellipse’s major axis, thearch is extracted by cutting the ellipse at the position y =ymean + (3/5) · Hellipse. This positioning is due to the fact thatbackground area (non-specific binding) must be located at afixed distance from the striatum, to be unaffected by the emittedradioactivity [31].

Therefore, the background activity is estimated by the countswithin the background ROI Cbackground defined as:

Cbackground =slicesup∑

k=sliceinf

∑

i,j∈archellipse

v(i, j) (6)

where sliceinf and slicesup are as defined previously, whereasarchellipse is the ROI containing the background activity es-timated within the arch of the ellipse previously described.According to the formulas (2) and (3), the striatum activity ismeasured as:

RLeft_Striatum =CLeft_Striatum − Cbackground

Cbackground(7)

RRight_Striatum =CRight_Striatum − Cbackground

Cbackground. (8)

F. System’s GUI

The application Graphical User Interface (GUI) has beendesigned as a floating shell, written in C++/C#, integrated intothe ClearCanvas (CC) Workstation tool,3 so that the operatorcan easily load a study into the provided CC Image Viewerand run the plugin by using the appropriate toolbar button.The provided tool’s GUI consists of three main sections: ImageEnhancement [Fig. 12(a)], which aims at removing noise af-fecting the SPECT images, Striatum Segmentation [Fig. 12(b)]that aims at identifying the striatum in each slice where it isvisible and at providing the isovalue for the further 3-DStriatumRendering [Fig. 12(c)], where the measurements of the ratiosR of the striatum activity are carried out. In the 3-D StriatumRendering section, the surface of the data under examination iscompared with a model (displayed in yellow) depicting howthe striatum should look like in healthy patients, built fromusing a the Alderson RSD phantom. To ensure a better softwareusability, the operator is allowed to perform the completeworkflow both in automatic and manual mode. In the manualmode, the implemented GUI offers some additional controls forsetting the input parameters, such as thresholds (or isovalue),required by the proposed method. Moreover, it is also possibleto configure a single slice or a subset of slices to be processed,instead of the whole data set. The 3-D rendering procedureautomatically loads the pre-configured phantom data set to berendered on the scene together with the patient’s study. Once thepatient’s data set has been processed, generic information andmeasurements on the striatum activity are displayed. In detail,the displayed information is:

• Frame Range: this value indicates the set of slices con-cerning the striatal area;

• Isovalue: the isovalue used for the patient’s striatum sur-face rendering;

• CLeft−Striatum: Left Striatum Count, indicates the countsmeasured in the left striatum area;

• CRight−Striatum: Right Striatum Count, indicates thecounts measured in the right striatum area;

• Counts RatioRStriatum of both left and right striatum.

V. EXPERIMENTAL RESULTS

The experimental results are so organized: firstly, the accu-racy of the 3-D reconstruction method in estimating striatumuptake using the R ratio and striatum volume is evaluated usinga phantom study and is compared with the BET algorithmproposed in [8]. The comparison with the ray casting algorithmwas not carried out because it requires many parameters andan optimal setting of these parameters for all the images usedin this work was not possible. Moreover, the processing timefor volume rendering in the ray casting method was 30 timesbigger than the one obtained with the proposed method (lessthan one second with a PC with a CPU Intel Core 2 Duo T7700with 2GB of RAM). Each 123I FP-CIT SPECT study comes as128 × 128 multi-frame (set of slices) DICOMs, with a slicespacing of 3.39 mm, i.e., each voxel corresponds to 38.95 mm3.

3www.clearcanvas.ca

3276 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 60, NO. 10, OCTOBER 2011

Fig. 12. Graphical User Interface of the proposed tool. (a) Image Enhancement Section. (b) Striatum Segmentation Section. (c) Surface Rendering Section.

TABLE IICOMPARISON BETWEEN THE R VALUES MEASURED BY OUR METHOD,

THE BET ALGORITHM METHOD, AND THE TRUE R VALUES (TR)OBTAINED FROM THE ALDERSON RSD PHANTOM. MRS AND

MLS INDICATE, RESPECTIVELY, THE MEASURED R VALUES

FOR RIGHT AND LEFT STRIATUM

Finally, a data set of 128 patients is used for assessing thediagnostic capability of the proposed measurement method.

A. Phantom Study Results

To validate the 3-D model reconstruction, the anthropomor-phic Alderson RSD4 phantom was used. In this phantom the1300 ml of the large brain volume was first filled with half aliter of distilled water. Then, 1 ml of a 10-ml solution containing6500 kBq (Becquerel)/ml of 123I-iodide solutions was added toserve as background (cortical) activity. After carefully mixingthe contents, the volume was entirely filled using distilledwater. This procedure resulted in an activity concentration ofapproximately 5 kBq/ml. Each striatum was, finally, filled with11.5 ml of a 25-ml solution containing 40 kBq/ml of123I-iodide. Afterwards, four acquisitions were performed atdifferent times. By this procedure, true R values (as defined informulas (7) and (8), i.e., ((Striatum-Background)/Background)were 7 for filling with 40 kBq/ml. The R measurements ob-tained by our method are compared with the true R values fromthe phantom study and with the results obtained by using theBET algorithm and are reported in Table II.

Let us note that the different measures obtained by ourmethod refer to different acquisitions, i.e., our method is notaffected by any variability in R measurements, in fact, if werepeat the measurements on the same acquisition, we obtainexactly the same values. The average error in the measurementof R (for left and right striatum) is 0.11 ± 0.09, whereas theone obtained with BET algorithm is 0.56 ± 0.33. This is a veryencouraging result, especially if compared with other existingsystems such as the one proposed by Calvini et al. [31], wherethe error on the same phantom was 0.85 ± 0.30.

4www.elimpex.com

TABLE IIISTRIATUM VOLUME’S MEASUREMENTS: COMPARISON BETWEEN

OUR METHOD AND THE ALDERSON RSD PHANTOM. MVSTANDS FOR MEASURED VOLUME IN mm3 AND IN ALL THE

ACQUISITIONS THE TRUE VOLUME WAS OF 22 800 mm3

TABLE IVR MEASUREMENTS CARRIED OUT WITH OUR METHOD ON THE DATA SET

WITH 128 PATIENTS WHERE μ IS THE MEAN, WHEREAS σ IS THE

STANDARD DEVIATION. T IS THE TOTAL NUMBER OF PATIENTS

AND Δ IS THE AVERAGE DIFFERENCE

Another evaluation was carried out on the measurements ofthe striatum volume. The Alderson RSD phantom is providedwith a striatum, whose volume is of 22.8 ml that correspondsto 22 800 mm3. The measurements of the volumes for thefour phantom’s data acquisitions and the comparison with themeasurements obtained with the BET algorithm are shown inTable III. Our approach achieves an average error in striatumvolume measurement of 177.32 ± 324.07 mm3, which corre-sponds to a percentage of error of about 0.77% ± 1.42% muchlower than the values obtained with the BET algorithm (averageerror in striatum volume measurement of 1145.76 ± 898.51, inpercentage 5.02% ± 3.94%) and the estimated range of error([−7.2%–8.4%]) in volume measurements of the existing 3-Dmethods for medical imaging reported by He et al. [35].

B. Patient’s Study Results

The manual ROI drawing and visual assessment method-ology (described in Section II) for nigrostriatal uptake mea-surements allows nuclear medicine physicians to diagnose onlyParkinson Disease (PD), i.e., by using reference values for theratios R (computed on the striatum), they are able to say if apatient is normal or affected by PD. For the patients’ studyevaluation, we used the aforementioned 128 patients data set

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Fig. 13. Scatterplot of R values obtained by the proposed method while measuring the striatum activity in NRM and PD Patients. An overlap (indicated with agrey area) is detected in the range [0–1.7].

Fig. 14. Comparison of the Histograms between the R values of the more affected side in PD patients and the ones in NRM patients. As it is possible to noticeboth histograms have values in the range [0–1.7]. In detail, in this range, we have identified 14 PD patients and 22 NRM patients.

TABLE VCLINICAL DIAGNOSIS AVAILABLE FOR A SUBSET OF THE

PATIENTS WITHIN THE Indecision Range

(128 123I FP − CIT SPECT images), diagnosed by an expertnuclear medicine physician as: 68 patients affected by PD and60 normal patients (NRM), i.e., healthy patients. The statisticsof the measurements for NRM patients and PD patients ob-tained with the proposed method are reported in Table IV. Indetail, the table shows the statistics of the striatum/backgroundratio 1) of the left and right striatum for NRM patients and2) of the more affected and less affected striatum (since inPD patients there is an evident asymmetry between the twostriatum) for PD patients.

Fig. 13 shows the scatterplot of the R (striatum activity)values obtained in PD patients (whose at least one side of thestriatum was damaged) and in NRM Patients. The R values in

TABLE VIMEAN (μ)AND STANDARD DEVIATION (σ) OF THE R VALUES OF

STRIATUM FOR DIFFERENT DISEASES

TABLE VIIIDENTIFIED RANGES OF R FOR SUPPORTING DISEASE DIAGNOSIS

NRM patients vary in the range [0–4.81], whereas the onesfor PD patients vary, respectively, for the less affected sidein the range [−10.03–6.84] and for the more affected side inthe range [−18.97–1.7]. The scatterplot indicates an overlap ofthe R values of the NRM patients and the ones (computed on

3278 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 60, NO. 10, OCTOBER 2011

Fig. 15. Scatterplot of R values obtained by the proposed method. No overlap is detected.

the more affected side) of the PD patients in the range [0–1.7],that we called Indecision Range. This overlap is also shown bycomparing the histograms of the R values of the more affectedside in PD patients and the ones in NRM patients as depictedin Fig. 14. The number of patients whose R values were in theindecision range was: 22 out of 60 NRM patients and 14 outof 68 PD patients, as shown in these histograms. The nuclearmedicine physician assessed by visual inspection that 10 outof the 22 NRM patients and 8 out of the 14 PD patients withinthe Indecision Range had a striatum’s shape somewhat differentfrom their respective typical pattern. Additional clinical dataavailable for these cases indicated that the clinical diagnosispointed to other diseases than the ones typically diagnosedfrom 123I FP-CIT SPECT, i.e., Parkinson and Essential Tremor(Table V). In detail, 2 and 8 out of 10 normal patients wereclinically diagnosed, respectively, with Alzheimer and VascularParkinsonism, whereas 6 out of 8 PD patients were clinicallydiagnosed with Vascular Parkinsonism. Two of the PD patientsdid not have a clear clinical diagnosis.

The nuclear medicine physician also noticed that in theremaining 18 patients (12 NRM and 6 PD) within the indecisionrange, there was uncertainty in the clinical description betweenParkinson disease and Essential Tremor, since these diseasesshow similar clinical symptoms. Hence, the diagnosis of the36 patients within the indecision range was: 14 with VascularParkinsonism, 2 with Alzheimer, 18 with possible EssentialTremor and 2 with uncertain diagnosis. Table VI shows themean and the standard deviation of the R values obtained by ourquantitative method for the 34 patients with diagnosis withinthe indecision range. As it is possible to notice, ranges ofR with no overlap were identified. Moreover, the 18 patientswith possible Essential Tremor showed a range different fromthe PD’s range. This findings suggests possible criteria for thedifferential diagnosis between Parkinson and Essential Tremor.

Summing up, the quantitative R measurements over the setof 128 123I FP-CIT SPECT studies led us to identify disjointranges, as shown in Table VII that seem to be peculiar ofspecific neurological diseases and needs to be further validatedon a larger data set. After the identification of the above ranges,the scatterplot shown in Fig. 13 changes as depicted in Fig. 15.

VI. CONCLUDING REMARKS

In this paper, we have proposed a fully automatic method forthe quantification of the nigrostriatal uptake by 3-D striatumactivity surface rendering. The proposed system is highly repro-ducible and observer independent and also shows encouragingresults in the identification of 123Ioflupane uptake both in aphantom study and in a set of 128 patients. In fact, the 3-Dsurface rendering method associated with the automatic iso-value detection method allowed us to measure the volume ofthe striatum in the Alderson RSD phantom with an averageaccuracy of 99.3% and the maximum error was of 662.8 mm3

that corresponds to about 17 voxels since any voxel is about38.9 mm3. Moreover, the proposed method led to the prelim-inary identification of novel ranges of the ratios R (striatumactivity) that are very promising for the differential diagnosisof Vascular Parkinsonism, Essential Tremor, Alzheimer, andParkinson. Of course, these ranges need to be validated by awider analysis of a larger data set for each disease. As futurework, we are working on a partial volume effect correctionmethod, that is required because of the low resolution of SPECTimage that does not always permit the clear identification of thetwo structures composing the striatum: Putamen and Caudate.Finally, since physical phenomena degrade the accuracy ofSPECT imaging resulting in a nonlinear relationship betweencounts and tracer uptake, a module for the attenuation correc-tion will be investigated.

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Alberto Faro received the Laurea degree in nuclearengineering from Politecnico of Milan, Milan, Italy,in 1971.

He is a Full Professor of artificial intelligencewith the Engineering Faculty, University of Catania,Catania, Italy, where he is also the Dean of theComputer Engineering degree. His current researchinterests include: dynamic systems theory of cog-nition, intelligent learning environments, computervision, and mobile computing.

3280 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 60, NO. 10, OCTOBER 2011

Daniela Giordano received the Laurea (grade110/110 cum laude) degree in electronic engineering,from the University of Catania, Catania, Italy, in1990 and the Ph.D. degree in educational technologyfrom Concordia University, Montreal, QC, Canadain 1998.

Since 2001, she is an Associate Professor ofinformation systems with the DIEEI Department,University of Catania. Her research activity has de-veloped along the following tracks: 1) knowledgemanagement; 2) image and signal processing; and 3)

advanced learning technologies.

Concetto Spampinato received the Laurea (grade110/110 cum laude) degree in computer engineeringand the Ph.D. degree from the University of Catania,Catania, Italy, in 2004 and 2008, respectively, wherehe is currently a Researcher Assistant.

His research interests include image and signalprocessing for motion detection systems in environ-mental applications, image enhancement and recon-struction for medical informatics, and content basedmultimedia retrieval.

Simona Ullo received the M.S. (cum laude) degreein informatics engineering from the University ofCatania, Catania, Italy, in 2010. She is currentlyworking toward the Ph.D. degree in computer visionat the Italian Institute of Technology (IIT), Genoa,Italy.

Her research interests include 3-D reconstructionand processing of bioimages, analysis of biologi-cal signals in neuronal networks, and eye-trackingtechnologies.

Angela Di Stefano received the Laurea degree inmedicine and surgery from the University of Catania,Catania, Italy, in 1980.

She did the residency in Nuclear Medicine atUniversity of Florence, Florence, Italy in 1983 andalso the residency in Radiology at the Universityof Catania in 1990. She is currently a Researcherwith the Italian Research Council of Catania,Neuroscience Department.