Images thresholding using ISODATA technique with gamma distribution

Image segmentation is a fundamental step in many applications of image processing. Many image segmentation techniques exist based on different methods such as classification-based methods, edge-based methods, region-based methods, and hybrid methods. The principal approach of segmentation is based on thresholding (classification) that is related to thresholds estimation problem. The ISODATA (Iterative Self-Organizing Data Analysis Technique) method is one of the classification-based methods in image segmentation. We assumed that the data in images is modeled by Gamma distribution. The objective of this paper is to explain a new method that combines Gamma distribution with the technique of ISODATA. The algorithm has two phases: splitting using Gamma distribution then merging which are done based on some predefined parameters. Experimental results showed good segmentation for artificial and real images.     

Towards an early diagnosis of Alzheimer disease: a precise and parallel image segmentation approach via derived hybrid cross entropy thresholding method

Alzheimer’s disease (AD) is an irreversible and progressive brain disease causing brain degenerative disorder and dementia. An early diagnosis of AD provides the individual an opportunity to participate in clinical trials. Computer Aided Diagnosis (CAD) system in the health care sector has been widely used and plays an important role in detecting such diseases. However, the main challenge of such systems is through identifying the region of interest obtained through precise segmentation. This paper attempts to solve the segmentation issue by developing a precise image segmentation model. The proposed model used a derivation of a hybrid cross entropy thresholding technique for the precise extraction of infected regions. In other words, a novel segmentation methodology has been proposed using the output derivation of both Gamma and Gaussian distributions. Moreover, to tackle the performance and time-consuming problems in digital image segmentation, a parallel boosting methodology has been developed and implemented. Through using the ADNI, OASIS, and MIRIAD benchmark datasets, the experimentation results validate the effectiveness of the proposed model through achieving more than 90% accuracy with 2x times speed improvement compared to other benchmark segmentation methods.