This paper proposes an automated method to identify abnormalities by exploiting symmetrical property features in Computed Tomography (CT) brain images. This method consists of two main steps; symmetrical axis detection and rule based abnormalities detection. Based on the principle axis theorem, any tilted intracranial is firstly corrected before symmetrical axis is generated. Then, segmented CT brains intracranial are divided into left half and right half used to produce possible feature vectors. Size (area) and location (centroid) of the abnormalities are chosen as main features for the development of the rule based abnormalities detection system. This experimental work uses twenty abnormal and eighty normal CT brain images and performance of proposed method is evaluated in term of sensitivity and specificity. It shows that the proposed automated method using symmetrical features proved to be efficient and accurate, and gives reliable results for every CT brain image tested.

Defining region of interests (ROIs) containing abnormal lesions on digital mammograms is the first step in many Computer-Aided-Diagnosis (CAD) systems for the classification of early signs of breast cancer as malignant or benign. The motivation of this paper is to study the efficiency of automated methods used in clustered microcalcifications (MCCs) detection module of a proposed CAD system. The proposed methods are based on several image processing concepts, such as morphological processing, fractal analysis, adaptive wavelet transform, local maxima detection and high-order statistics (HOS) tests. We applied these methods on a set of MIAS database mammograms. The mammograms consisted of two groups, which were cancerous (clustered MCCs) and non-cancerous (normal) and they were digitized at a size of 1024 by 1024 with 256 gray levels. The results showed that the efficiency of HOS test, fractal analysis and morphological approach were 99%, 92% and 74%, respectively. It was proven that the HOS test was the most efficient, and gave reliable results for every mammogram tested.

This paper presents an automated computed tomography brain segmentation approach used to segment intracranial into brain matters and cerebrospinal fluid in order to detect any asymmetry present. Intracranial midline is used as reference axial where left and right segmented regions are subjectively compared. Two-level Otsu multi-thresholding method has been developed and applied to 213 abnormal cases of serial computed tomography brain images of thirty one patients. Prior to that, multilevel Fuzzy C-Means is used to extract the intracranial from background and skull. The segmented regions found to be very useful in providing information regarding normal and abnormal structures in the intracranial where any asymmetry detected would indicate high probability of abnormalities. This approach proved to effectively isolate important homogenous regions of computed tomography brain images from which extracted features would provide a strong basis in the application of content-based medical image retrieval.

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