Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...

Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human expert is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation compensation, motion detection, and correct...

Medical images analysis is becoming increasingly important in clinical applications. One of the active resarch areas in medical image analysis is image coregistration which involves information fusion of tomographic diagnostic images obtained from different modalities. We present a novel MRI and PET brain image coregistration technique using binary correlation matching based on multiple image f...

This paper presents a dominant color descriptor technique for medical image retrieval. The medical image system will collect and store into medical database. The purpose of dominant color descriptor (DCD) technique is to retrieve medical image and to display similar image using queried image. First, this technique will search and retrieve medical image based on keyword entered by user. After im...

Background: Nowadays, image de-noising plays a very important role in medical analysis applications and pre-processing step. Many filters were designed for image processing, assuming a specific noise distribution, so the images which are acquired by different medical imaging modalities must be out of the noise. Objectives: This study has focused on the sequence filters which are selected ...

We consider partnerships among risk-averse professionals endowed with (i) a risky and personally-costly production technology and (ii) a personallycostly monitoring technology providing contractible noisy signals about partners’productive e¤orts. Partners shirk both production and monitoring tasks because e¤orts are unobservable. We charaterize optimal partnership size, pro…t shares and incenti...

In this paper, we explored a learning approach which combines di erent learning methods in inductive logic programming (ILP) to allow a learner to produce more expressive hypotheses than that of each individual learner. Such a learning approach may be useful when the performance of the task depends on solving a large amount of classication problems and each has its own characteristics which may...

Radial basis neural (RBF) networks provide an excellent solution to many pattern recognition and classi cation problems. However, RBF networks are also a local representation technique that enables the easy conversion of the hidden units into symbolic rules. This paper examines rules extracted from RBF networks. We use the iris ower classication task and a vibration diagnosis classi cation task...

texture image analysis is one of the most important working realms of imageprocessing in medical sciences and industry. up to present, different approacheshave been proposed for segmentation of texture images. in this paper, we offeredunsupervised texture image segmentation based on markov random field (mrf)model. first, we used gabor filter with different parameters’ (frequency,orientation) va...