International Journal of Computer
Trends and Technology

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Volume 4 | Issue 4 | Year 2013 | Article Id. IJCTT-V4I4P105 | DOI : https://doi.org/10.14445/22312803/IJCTT-V4I4P105

Algorithms for Computer Aided Diagnosis – An Overview

Dr.A.Padmapriya, K.Silamboli Chella Maragatham

Citation :

Dr.A.Padmapriya, K.Silamboli Chella Maragatham, "Algorithms for Computer Aided Diagnosis – An Overview," International Journal of Computer Trends and Technology (IJCTT), vol. 4, no. 4, pp. 472-478, 2013. Crossref, https://doi.org/10.14445/22312803/ IJCTT-V4I4P105

Abstract

In medicine, two types of resources are becoming widely used: the Content-based Image Retrieval (CBIR) and the Computer- Aided Diagnosis (CAD) systems. The purpose of CAD is to increase the accuracy of diagnosis, as well as to improve the consistency of image interpretation by using the computer results as a second opinion. Similar to CAD systems, CBIR uses information extracted from images to represent them. However, the main purpose of a CBIR system is to retrieve ‘‘cases” or images similar to a given one. Analyzing past similar cases and their reports can improve the radiologist’s confidence on elaborating a new image report, besides making the training and the diagnosing process faster. Moreover, CAD and CBIR systems are very useful in medicine teaching. Currently, image mining has been focused by many researchers in data mining and information retrieval fields and has achieved prominent results. A major challenge in the image mining field is to effectively relate low-level features (automatically extracted from image pixels) to high-level semantics based on the human perception. Association rules has been successfully applied to other research areas, e.g. business, and can reveal interesting patterns relating low-level and high-level image data as well. In this work, association rules are employed to support both CAD and CBIR systems.

Keywords

Image mining, Association rules, Classification, Prediction.

References

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