The importance of neuro-imaging as one of the biomarkers for diagnosis and prognosis of pathologies and traumatic cases is well established. Doctors routinely perform linear measurements on neuro-images to ascertain severity and extent of the pathology or trauma from significant anatomical changes. However, it is a tedious and time consuming process and manually assessing and reporting on large volume of data is fraught with errors and variation. In this paper we present a novel technique for segmentation of significant anatomical landmarks using artificial neural networks and estimation of various ratios and indices performed on brain CT scans. The proposed method is efficient and robust in detecting and measuring sizes of anatomical structures on non-contrast CT scans and has been evaluated on images from subjects with ages between 5 to 85 years. Results show that our method has average ICC of ≥0.97 and, hence, can be used in processing data for further use in research and clinical environment

Qureshi, Adnan Nabeel Abid

Schetinin, Vitaly

English

University of Bedfordshire Repository

QURESHI, SCHETININ: SEGMENTATION AND ESTIMATION OF BRAIN INDICES 1Computer-Aided Segmentation andEstimation of Indices in Brain CT ScansAdnan Qureshiadnan.qureshi@beds.ac.ukVitaly Schetininvitaly.schetinin@beds.ac.ukInstitute for Research in ApplicableComputingUniversity of BedfordshireLuton, UK.AbstractThe importance of neuro-imaging as one of the biomarkers for diagnosis and prog-nosis of pathologies and traumatic cases is well established. Doctors routinely performlinear measurements on neuro-images to ascertain severity and extent of the pathology ortrauma from significant anatomical changes. However, it is a tedious and time consum-ing process and manually assessing and reporting on large volume of data is fraught witherrors and variation. In this paper we present a novel technique for segmentation of sig-nificant anatomical landmarks using artificial neural networks and estimation of variousratios and indices performed on brain CT scans. The proposed method is efficient androbust in detecting and measuring sizes of anatomical structures on noncontrast CT scansand has been evaluated on images from subjects with ages between 5 to 85 years. Resultsshow that our method has average ICC of ≥ 0.97 and, hence, can be used in processingdata for further use in research and clinical environment.1 Overview and problem statementLinear measurements on axial CT scans provide clinicians and surgeons opportunity to ascer-tain differential diagnoses of neuropsychiatric disorders, outcomes of clinical and surgicalinterventions, geriatric changes and deleterious effects of drugs. Quantitative assessmentof neuro-images is an effective approach to reveal structural changes in conditions such asAlzheimer’s disease (AD), Schizophrenia, Huntington’s disease, hydrocephalus and manyother neurological and psychiatric disorders [1, 2, 11, 12]. The typical measurements per-formed on the axial CT scans (fig. 1a, details in table 1) are used to estimate indices andratios such as Evan’s Ratio (ER), Bifrontal Index (BFI), Bicaudate Index (BCI), Cella MediaIndex (CMI), Frontal Horn Index (FHI), Ventricular index (VI), Huckman number (HN) and3rd ventricle width (V3) [3]. Manual measurements of regions of interest (ROI) are still con-sidered ’gold standard’, however, these are time consuming and a robust and efficient methodis required to assist the researchers and to our knowledge, there is no automated system forperforming measurements and calculation of these from CT scans.Computer aided quantitative radiology and volumetry studies of the human anatomy andpathology have been undertaken by many researchers and a surfeit of image segmentationmethods have been proposed [10]. However, in medical images an ROI can have dissimi-larity of pixel intensity, inhomogeniety of background contribution and noise, and spatiallyc© 2014. The copyright of this document resides with its authors.It may be distributed unchanged freely in print or electronic forms.2 QURESHI, SCHETININ: SEGMENTATION AND ESTIMATION OF BRAIN INDICES(a) (b) (c)Figure 1: Measurements on Axial CT Scans (a) and Manual Labelling (b, c).blind methods in these cases can result in disjoint, quasi-homogeneous regions [9]. To pre-serve the spatial relationship between the pixels and neighbourhood information, spatiallyguided techniques such as region growing and merging, active contours and level sets showbetter results [10].The extensive and tenable integration of computational intelligence in medical problemscan be attributed to the fact that these systems can adaptively learn and optimize the rela-tionship between inputs and outputs [4, 5, 6, 7, 8]. Medical images e.g., CT scans, usuallyhave inhomogeniety of background and noise; and recognition of ROI in images with similarcharacteristics using ANN can give plausible results [13]. In addition, ANN’s can be trainedusing a few images which have been manually annotated and labelled in order to learn torecognize the ROI.2 MethodologyThe proposed methodology uses ANN to segment the input images into ROI. Measurementsof landmark regions are then used for calculating the various ratios and indices. The inputimages are DICOM (Digital Imaging and Communications in Medicine) files and the trainingset has been manually segmented and measured by radiology experts (Figure 1b, 1c). Thedataset used for training, validation and testing consists of noncontrast CT studies fromdifferent subjects. The studies include subjects of both genders and between ages 5 to 85years. Our method is used on axial CT slices.2.1 Correction of Orientation of HeadThe first step is to align the head into proper orientation on the CT image to ensure that thereference points for measurements are properly identified. This is done by demarcating thebony protuberance of the anterior and posterior falx cerebri attachments as these landmarksare usually not affected in traumatic and pathological cases in which the brain tissue mayshow abnormalities. The skull boundaries are extracted using Canny edge detection withσ = 2 and threshold = 0.5 in our experiments, and properties of connected regions aremeasured. Then the curve of the skull bone is modelled as a function of the location andheight of pixels and the extrema represent the protuberances which are used to find any shiftPshi f t in the orientation.QURESHI, SCHETININ: SEGMENTATION AND ESTIMATION OF BRAIN INDICES 3(a) (b) (c)Figure 2: Correction of orientation of CT Scans.Taking arctan of the Pshi f t with reference to the skull centroid calculated earlier, gives therotation angle θ ◦ to be corrected (Figure 2). An ideal midline can also be drawn using skullcentroid which, in normal cases, would pass through the septum pellucidum. Identifyingthe septum pellucidum and its location with reference to skull can be used for measuring themidline shift which is an important clinical feature in assessment of traumatic or pathologicalconditions.2.2 Detection of Candidates for MeasurementsAn input image is represented as an (n×m) matrix X = {xi j}, i = 1 . . .n, j = 1 . . .m, whosepixels can either belong to a region of interest class Ω0 or to a non region Ω1. A functionui j = φ (X ; i, j) reflects the brightness of the pixel xi j influenced constantly or variably bythe neighbourhood. The function is estimated in a rectangular window P, which is a (k× k)matrix containing the central pixel xi j and r−1 nearest pixels, where r = k2. Then by slidingthe window P through the image X , every central pixel xi j can be assigned to one of the twoclasses Ω0 or Ω1. The sliding window transforms the image X into an r×q matrix Z whereq = (n− k+1)(m− k+1) and the central element of a vector z ∈ Z, hence, represents thepixel xi j of the input image. The matrix Z is fed to the ANN with one hidden layer andthe output neuron makes a decision yc = {0,1},c = 1 . . .q, on the central element of cthcolumn vector z(c), thus, classifying the image pixels to classes Ω0 or Ω1 as proposed by[13]. The ANN is trained with standard back propagation technique aiming to minimize anerror function ε for a given number of training epochs. We use images whose pixels aremanually labelled and assigned either to class Ω0 or Ω1 by a radiology expert Fig. 3b andthe ANN is trained (Fig. 3c) to approximate these. The approach is implemented to identifyboth ventricles and skull as ROI for subsequent measurements and estimations of indices.The output from the neural network classifies pixels of the input image into ROI asshown in Fig. 3e. In this binarized image, the discontinuities within the regions and thenon-smooth boundaries are further refined using active contours using output of ANN forautomatic seeding. The values of curve length µ and curvature κ are empirically estimated inour experiments to ensure a good approximation of the edges of regions. This step generatesa binary image as output with the skull and ventricles’ boundaries clearly demarcated as inFig. 3f and 3g respectively.2.3 Measurements and Estimation of IndicesThe CT scan studies are viewed using ’brain window’ (width = 80, length = 40) with imagesize of 512× 512 pixels and a reference ruler is overlaid on the images to calibrate our4 QURESHI, SCHETININ: SEGMENTATION AND ESTIMATION OF BRAIN INDICES(a) Input image (b) Manual labelling (c) ANN Output(d) Input Image (e) Segmentation (f) Skull (g) VentriclesFigure 3: Segmentation of the ROI in Brain CT Scans.measurement algorithm. An expert then provides multiple reference measurements in aninteractive session using a simple user interface and the averages are stored as benchmarksPcal for subsequent automatic measurements. There is an option to use Metric or Imperialunits for measurements. The contiguous image regions from step 2.2 are used to determinemeasurements such as extrema, centroid, area, major and minor axes. The values of extremaare used to draw reference points (Fig. 1a) around the ROI using same approach as section2.1 and the number of pixels between relevant points is divided by Pcal to give measurements.To estimate the indices, the distance between relevant reference points is counted in terms ofpixels and appropriate formula is applied from atlas [3].3 Experimentation ResultsThe technique has been applied on 48 noncontrast CT studies from different subjects withages between 5 to 85 years including 14 subjects from the National Cancer Institute pub-lic dataset. Two radiology experts independently performed manual segmentation and cal-culated ratios and indices to be used for training and evaluation in our experiments. Thesubjects’ personal information was anonymized and only age and gender data were retained.Measurements of the maximum frontal horn diameter (A), inner skull diameter at the level offrontal horns (D), maximum inner skull diameter (F), outer skull diameter at the frontal horns(G), maximum outer skull diameter (H) and minimum width of the later ventricles separatedby the septum (I) were made by experts. Our method repeated the same linear measurementsafter segmenting the ROI and using reference points and calibration information. The resultswere compared with the measures in noncontrast CT scans from different age groups andshowed ICC ≥ 0.97. Correction of orientation of head showed 85% sensitivity and 75%,while detection of brain midline shift > 5mm showed sensitivity and specificity of 80% and86% respectively. The intraclass correlation coefficient (ICC) was calculated using MedCalcto compare the results with experts to ascertain reliability of measurements. Linear mea-surements show ICC range from 0.97 to 0.99 and the results are given in table 1. For boththe CMI and ER estimations, ICC was 0.98 compared to the experts. The optimum testingperformance of 88.9% of the ANN was observed with 10 hidden neurons to segment theQURESHI, SCHETININ: SEGMENTATION AND ESTIMATION OF BRAIN INDICES 5Label Measurement ICCA Maximum frontal horn diameter 0.99D Inner skull diameter at the level of thefrontal horns0.97F Maximum inner skull diameter 0.97G Outer skull diameter at the frontal horns 0.97H Maximum outer skull diameter 0.98I Minimum width of the lateral ventriclesseparated by the septum (cella media)0.98Table 1: Measurements on the axial CT scans(a) Transtentorial Arachnoid cyst (b) Metastatic lesionsFigure 4: Cases excluded from experiments.image using 70% training, 15% validation and 15% test samples. Increasing or decreasingthe number of hidden neurons in ANN did not significantly improve the performance andthe average performance was 87.4− 88.9% in training and testing. The sliding window insection 2.2 was evaluated with varying sizes of 3×3, 5×5 and 7×7 neighbourhood pixels,however, the optimum output was achieved with 5× 5 pixels windows for extracting fea-tures. The experiments were performed using Matlab R2013b on Mac OS X and Windows 7platforms. The average time taken by our method to perform segmentation and estimationsof indices and ratios was≤ 20 seconds because the algorithm performs the segmentation andmeasurements in toto without performance degradation. CT studies of patients with grossdeformity of the brain structure due to either neoplastic metastasis, pathology or trauma gaveless satisfactory estimations and resulting measurements were amiss. These 2 of the 34 cases(fig. 4) were excluded from the performance analyses after preliminary experiments.4 ConclusionManual demarcation of ROI by expert radiologists is still considered gold standard, however,our proposed system has shown plausible results with 87.4−88.9% accuracy with ANN insegmenting the ROI which are used as reference points for estimating various brain indices.The error in linear measurements on noncontrast axial CT scans was less than ±1mm andICC results were ≥ 0.97 compared with experts. Correction of orientation of head and de-tection of midline shift > 5mm showed sensitivity of 85% and 80% and specificity of 75%and 86% respectively. The average time taken for measurements was also significantly re-duced. Currently, the CT slice with pertinent anatomical landmarks is manually input tothe system for measurements. However, we are working on an algorithm to automaticallyselect the representative slice from the complete study of the subject. Interpretation of the6 QURESHI, SCHETININ: SEGMENTATION AND ESTIMATION OF BRAIN INDICESmedical images by the radiologists is fraught with errors and variations which represent theweakest aspect of clinical imaging. Based on the results of our method, we propose thatintegration of automated systems in clinical and research settings can significantly reducethe radiologists’ workload and inter-observer variability in assessing normal development,ageing, pathological and traumatic cases.References[1] S. Barker-Collo, M. Kahan, and N. Starkey. Computerised tomography indices ofraised intracranial pressure and traumatic brain injury severity in a new zealand sample.The New Zealand medical journal, 125(1360):92, 2012.[2] E. González-Reimers and F. Santolaria-Fernández. Brain atrophy in alcoholics. InHandbook of Behavior, Food and Nutrition, pages 2993–3010. Springer, 2011.[3] T. Keats and C. Sistrom. Atlas of radiologic measurement. Mosby Inc, 2001.[4] T. Kondo and J. Ueno. 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Application of neural networks in medical image processing.

Atlas of radiologic measurement.

Brain atrophy in alcoholics.

Color image segmentation based on mean shift and normalized cuts.

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Feedback GMDH-type neural network and its application to medical image analysis of the liver cancer.

Filament recognition in solar images with the neural network technique.

Learning polynomial networks for classification of clinical electroencephalograms.

Machine learning for medical diagnosis: history, state of the art and perspective.

Normative values for selected linear indices of the intracranial fluid spaces based on ct images of the head in children.

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Usefulness of computed tomography linear measurements in diagnosing alzheimer’s disease.

Computer-aided segmentation and estimation of indices in brain CT scans

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Computer-aided segmentation and estimation of indices in brain CT scans

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