Abdolvahab Ehsani Rad

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.

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.

Morphological region-based initial contour algorithm for level set methods in image segmentation

Initial Contour (IC) is the essential step in level set image segmentation methods due to start the efficient process. However, the main issue with IC is how to generate the automatic technique in order to reduce the human interaction and moreover, suitable IC to have accurate result. In this paper a new technique which we called Morphological Region-Based Initial Contour (MRBIC), is proposed to overcome this issue. The idea is to generate the most suitable IC since the manual initialization of the level set function surface is a well-known drawback for accurate segmentation which has dependency on selection of IC and wrong selection will affect the result. We have utilized the statistical and morphological information inside and outside the contour to establish a region-based map function. This function is able to find the suitable IC on images to perform by level set methods. Experiments on synthetic and real images demonstrate the robustness of segmentation process using MRBIC method even on noisy images and with weak boundary. Furthermore, computational cost of segmentation process will be reduced using MRBIC.

Computer-Aided Dental Caries Detection System from X-Ray Images

The early detection of disease is one of important matter of diagnostic imaging. In this paper we developed a system to analysis the dental x-ray images and diagnosis the tooth which has abnormalities of caries. Enhancement applied to improve the quality of x-ray images and Thresholding method performed to simplify the images. Segmentation has been done by applying the integral projection technique to extract the individual tooth and therefore feature map of tooth surface generated to analysis and detection process. Nevertheless, experiments show the accurate segmentation and caries detection with proposed method which achieves high accuracy and promising result.

Automatic computer-aided caries detection from dental x-ray images using intelligent level set

Dental diseases have high risk of affection across the globe and mostly in adult population. The analysis of dental X-ray images has some difficulties in comparison to other medical images, which makes segmentation a more challenging process. One of the most important and yet largely unsolved issues in the level set method framework is the efficiency of signed force, speed function and initial contour (IC) generation. In this paper, a new segmentation method based on level set (LS) is proposed in two phases; IC generation using morphological information of image and intelligent level set segmentation utilizing motion filtering and back propagation neural network. The segmentation results are efficient and accurate as compared to other studies. The new approach to isolate each segmented teeth image is proposed by employing integral projection technique and feature map designed for each tooth to extract the local information and therefore to detect caries area. The achieved overall performance of the proposed segmentation method was evaluated at 120 periapical dental radiograph (X-ray), with images at 90% and the detection accuracy of 98%.