The Relevance of Computer-Aided-Diagnosis Systems in Microscopy Applications to Medicine and Biology

2008 ◽  
pp. 1175-1182 ◽  
Author(s):  
Paolo Soda ◽  
Giulio Iannello
Keyword(s):  
Image Segmentation ◽  
Short Review ◽  
Healthcare Systems ◽  
Future Trend ◽  
Structural Approach ◽  
Semantic Interpretation ◽  
Review Of The Literature ◽  
Computer Aided ◽  
Fluorescence Images ◽  
Aided Diagnosis

In this article, we discuss the use of computerbased systems in microscopy, focusing on cytological images. We initially present recent results on image segmentation, and then we argue that it makes sense moving from a structural approach to a semantic interpretation of micrographs. In this respect, we focus on the relevance of using CAD tools to overcome the current limitations of microscopy, investigating several peculiar objectives of such systems. A short review of the literature demonstrates that the development of a flexible CAD applicable to various working scenarios is a future trend in microscopy healthcare systems. To support our position, we briefly describe a tool that analyzes and classifies fluorescence images.

scholarly journals A Comparative Analysis of Depth Computation of Leukaemia Images using a Refined Bit Plane and Uncertainty Based Clustering Techniques

2015 ◽  
Vol 15 (1) ◽  
pp. 126-146
Author(s):  
Swarnalatha Purushotham ◽  
B. K. Tripathy
Keyword(s):  
Image Segmentation ◽  
Comparative Analysis ◽  
Medical Diagnosis ◽  
Clustering Algorithm ◽  
Time Estimation ◽  
The Other ◽  
Clustering Techniques ◽  
Computer Aided ◽  
Bit Plane ◽  
Aided Diagnosis

Abstract Several image segmentation techniques have been developed over the years to analyze the characteristics of images. Among these, the uncertainty based approaches and their hybrids have been found to be more efficient than the conventional and individual ones. Very recently, a hybrid clustering algorithm, called Rough Intuitionistic Fuzzy C-Means (RIFCM) was proposed by the authors and proved to be more efficient than the conventional and other algorithms applied in this direction, using various datasets. Besides, in order to remove noise from the images, a Refined Bit Plane (RBP) algorithm was introduced by us. In this paper we use a combination of the RBP and RIFCM to propose an approach and apply it to leukemia images. The aim of the paper is twofold. First, it establishes the superiority of our approach in medical diagnosis in comparison to most of the conventional, as well as uncertainty based approaches. The other objective is to provide a computer-aided diagnosis system that will assist the doctors in evaluating medical images in general, and also in easy and better assessment of the disease in leukaemia patients. We have applied several measures like DB-index, D-index, RMSE, PSNR, time estimation in depth computation and histogram analysis to support our conclusions.

scholarly journals A SURVEY OF ULTRASONOGRAPHY BREAST CANCER IMAGE SEGMENTATION TECHNIQUES

2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Jwan N. Saeed
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Image Segmentation ◽  
Active Contour ◽  
Breast Ultrasound ◽  
Common Cause ◽  
Cad Systems ◽  
Detection Techniques ◽  
Computer Aided ◽  
Aided Diagnosis

The most common cause of death among women globally is breast cancer. One of the key strategies to reduce mortality associated with breast cancer is to develop effective early detection techniques. The segmentation of breast ultrasound (BUS) image in Computer-Aided Diagnosis (CAD) systems is critical and challenging. Image segmentation aims to represent the image in a simplified and more meaningful way while retaining crucial features for easier analysis. However, in the field of image processing, image segmentation is a tough task particularly in ultrasound (US) images due to challenges associated with their nature. This paper presents a survey on several techniques of ultrasonography images segmentation including threshold based, region based, watershed, active contour and learning based techniques, their merits, and demerits. This can provide significant insights for CAD developers or researchers to advance this field.

Pattern-Recognition: A Comparison of the Performance of Clinicians and Non-Clinicians - with a Note on the Performance of a Computer-Based System

1972 ◽  
Vol 11 (01) ◽  
pp. 32-37 ◽  
Author(s):  
F. T. DE DOMBAL ◽  
J. C. HORROCKS ◽  
J. R. STANILAND ◽  
P. J. GUILLOU
Keyword(s):  
Pattern Recognition ◽  
Computer System ◽  
Computer Aided Diagnosis ◽  
Clinical Interview ◽  
Computer Performance ◽  
Pattern Size ◽  
Computer Aided ◽  
Computer Based ◽  
Aided Diagnosis ◽  
Better Than

This paper describes a series of 10,500 attempts at »pattern-recognition« by two groups of humans and a computer based system. There was little difference between the performances of 11 clinicians and 11 other persons of comparable intellectual capability. Both groups’ performances were related to the pattern-size, the accuracy diminishing rapidly as the patterns grew larger. By contrast the computer system increased its accuracy as the patterns increased in size.It is suggested (a) that clinicians are very little better than others at pattem-recognition, (b) that the clinician is incapable of analysing on a probabilistic basis the data he collects during a traditional clinical interview and examination and (c) that the study emphasises once again a major difference between human and computer performance. The implications as - regards human- and computer-aided diagnosis are discussed.

Sign in / Sign up

Export Citation Format

Share Document