Mohd Shafry Mohd Rahim

Bio: Mohd Shafry Mohd Rahim is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Image segmentation & Feature extraction. The author has an hindex of 18, co-authored 186 publications receiving 1526 citations. Previous affiliations of Mohd Shafry Mohd Rahim include Multimedia University & Universiti Malaysia Terengganu.

Medical Image Segmentation Methods, Algorithms, and Applications

Abstract: Medical images have made a great impact on medicine, diagnosis, and treatment. The most important part of image processing is image segmentation. Many image segmentation methods for medical image analysis have been presented in this paper. In this paper, we have described the latest segmentation methods applied in medical image analysis. The advantages and disadvantages of each method are described besides examination of each algorithm with its application in Magnetic Resonance Imaging and Computed Tomography image analysis. Each algorithm is explained separately with its ability and features for the analysis of grey-level images. In order to evaluate the segmentation results, some popular benchmark measurements are presented in the final section.

A general review of human face detection including a study of neural networks and Haar feature-based cascade classifier in face detection

Abstract: Face detection is an interesting area in research application of computer vision and pattern recognition, especially during the past several years. It is also plays a vital role in surveillance systems which is the first steps in face recognition systems. The high degree of variation in the appearance of human faces causes the face detection as a complex problem in computer vision. The face detection systems aimed to decrease false positive rate and increase the accuracy of detecting face especially in complex background images. The main aim of this paper is to present an up-to-date review of face detection methods including feature-based, appearance-based, knowledge-based and template matching. Also, the study presents the effect of applying Haar-like features along with neural networks. We also conclude this paper with some discussions on how the work can be taken further.

Evaluation of current dental radiographs segmentation approaches in computer-aided applications

Abstract: With a wide variety researches on Image segmentation techniques in biomedical and bioinformatics area, it is important to analyze the performance of these approaches in specific problems. Image segmentation is one of the most significant processes of dental X-ray image analysis. Therefore, to obtain the proper result, it is required to perform the accurate and efficient segmentation approach which proved itself in the aspect of X-ray image segmentation. The aim of this review paper is to understand the different image segmentation approaches which have been used for dental X-ray image analysis over the past studies. In this paper, different available approaches of dental X-ray image segmentation, reviewed and their advantages, disadvantages, and limitations are discussed.

Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach.

Abstract: The most commonly injured ligament in the human body is an anterior cruciate ligament (ACL). ACL injury is standard among the football, basketball and soccer players. The study aims to detect anterior cruciate ligament injury in an early stage via efficient and thorough automatic magnetic resonance imaging without involving radiologists, through a deep learning method. The proposed approach in this paper used a customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing and data augmentation. The performance was evaluated using accuracy, sensitivity, specificity, precision and F1 score of our customized ResNet-14 deep learning architecture with hybrid class balancing and real-time data augmentation after 5-fold cross-validation, with results of 0.920%, 0.916%, 0.946%, 0.916% and 0.923%, respectively. For our proposed ResNet-14 CNN the average area under curves (AUCs) for healthy tear, partial tear and fully ruptured tear had results of 0.980%, 0.970%, and 0.999%, respectively. The proposing diagnostic results indicated that our model could be used to detect automatically and evaluate ACL injuries in athletes using the proposed deep-learning approach.

Digital Dental X-Ray Image Segmentation and Feature Extraction

Abstract: The process of analysis of such images is important in order to improve quantify medical imaging systems. It is significant to analysis the dental x-ray images we need features of image. In this paper we present a method for segmentation and feature extraction of dental x-ray images. The proposed method has been implemented by using level-set method for segmentation after image enhancement and illustrate contour for teeth to complete the segmentation step. Furthermore, we extracted multiple features of dental x-ray images using texture statistics techniques by gray-level co-occurrence matrix. Extracted data can perform to obtain the teeth measurements for automatic dental systems such human identification or dental diagnosis systems. Preparatory experiments show the significance of the proposed method to extract teeth from an x-ray image. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2655 Full Text: PDF

Computer and Robot Vision

Abstract: Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Hand and Mind: What Gestures Reveal about Thought

Abstract: Hand and Mind: What Gestures Reveal about Thought. David McNeill. Chicago and London: University of Chicago Press, 1992. 416 pp.

Prescribing in Dermatology: Skin cancer

Abstract: The incidence of skin cancer is increasing and nurses are in an ideal position to help patients prevent and identify the disease at an early stage.