Siti Salmah Yasiran

Seed point selection for seed-based region growing in segmenting microcalcifications

Seed-based region growing (SBRG) has been widely used as a segmentation method for medical images. The selection of initial seed point in SBRG is the crucial part before the segmentation process is carried out. Most of the region growing methods identify the seed point manually which involve human interaction and require prior information about the image. In this paper, an automated initial seed point selection for SBRG algorithm is proposed. The proposed method is tested on 50 mammogram images confirmed by a radiologist to consist microcalcifications. The performance is evaluated using Receiving Operator Curve (ROC) based on level of detection. Experimental results show that the method has successfully segmented the microcalcifications with 0.98 accuracy.

Efficiency of Enhanced Distance Active Contour (EDAC) for Microcalcifications Segmentation

In this paper the boundaries of microcalcifications in mammogram are segmented images by using the Distance Active Contour (DAC) method. However, the DAC requires longer computational time to finish the segmentation process. Thus, the Enhanced Distance Active Contour (EDAC) is proposed to overcome the problems. The efficiency is measured in terms of time lapse and number of iterations. Results obtained show that the EDAC has successfully reduced the processing time as well as the number of iterations. In addition to that, the boundaries of microcalcifications have been successfully segmented by the EDAC. It is also found that the efficiency of EDAC is better than the DAC.

Classification of breast abnormalities using artificial neural network

Classification is the process of recognition, differentiation and categorizing objects into groups. Breast abnormalities are calcifications which are tumor markers that indicate the presence of cancer in the breast. The aims of this research are to classify the types of breast abnormalities using artificial neural network (ANN) classifier and to evaluate the accuracy performance using receiver operating characteristics (ROC) curve. The methods used in this research are ANN for breast abnormalities classifications and Canny edge detector as a feature extraction method. Previously the ANN classifier provides only the number of benign and malignant cases without providing information for specific cases. However in this research, the type of abnormality for each image can be obtained. The existing MIAS MiniMammographic database classified the mammogram images into three features only namely characteristic of background tissues, class of abnormality and radius of abnormality. However, in this research three ot...

Performance comparison of Canny and Sobel edge detectors on Balloon Snake in segmenting masses

The most common problem in image processing is image segmentation. One of the methods which can segment an image with a high accuracy is Balloon Snake. It uses the energy minimization conception where it has a dynamic behavior that deforms from an initial position and converges to the boundary of the object in an image. However, we found that the accurateness always influences by the edge map detector that being used in implementing the Balloon Snake. Edge map detector is used to strengthen the boundary of the object before the object is segmented. Two popular edge map detectors are chosen in this research namely Canny and Sobel edge detector. Both edge detectors are implemented in Balloon Snake in segmenting 40 masses in breast ultrasound images. The pixel area traced by the two combination methods namely Canny and Balloon Snake and Sobel and Balloon Snake are evaluated. The accuracy is measured based on the percentage pixel area difference between radiologist and the both combination methods. It is found that the combination of using Canny and Balloon Snake give 3.5% percentage of pixel area difference which is smaller compared to the other combination which have 12.7% percentage pixel area difference.

Numerical solution of a linear Klein-Gordon equation

A new scheme of a linear inhomogeneous Klein-Gordon equation is developed by utilizing finite difference method incorporated with arithmetic mean averaging of functional values. This study considered the central time central space (CTCS) finite difference scheme incorporated with four points arithmetic mean averaging. In addition, the theoretical aspects of finite difference scheme are also considered such as stability, consistency and convergence. The von Neumann stability analysis method and Miller Norm Lemma are used to analyze the stability of the proposed scheme. The performance analysis shows the proposed scheme is stable, consistent and convergent. These theoretical analyses are verified by a numerical experiment. The comparison results shown the proposed scheme produces better accuracy rather than the standard CTCS scheme.

Microcalcifications segmentation using three edge detection techniques

Edge detection has been widely used especially in medical image processing field. In this paper we are comparing Sobel, Prewitt and Laplacian of Gaussian (LoG) edge detection techniques in segmenting the boundary of microcalcifications. The edge detection must satisfy the breast phantom scoring criteria before the segmentation phase is carried out. Then, all of the edge detection techniques are implemented in the Enhanced Distance Active Contour (EDAC) model for the segmentation process. Results obtained from Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve shows that the Prewitt edge detection has the highest value of AUC, followed by the Sobel and LoG which are 0.79, 0.72 and 0.71 respectively.

Comparison between GVF snake and ED snake in segmenting microcalcifications

Snake, active contour or deformable active contour has been widely used in medical image segmentation area. In this paper, comparison between Gradient Vector Flow (GVF) snake and Enhanced Distance (ED) snake in segmenting microcalcifications is carried out. The performance is measured based on actual area of the average percentage difference traced by expert radiologists. Results obtained shows that the values of average percentage difference for the GVF and ED snake are 4.3% and 6.68% respectively. These results indicate that the GVF snake has better performance with 95.7%.