An Efficient Multiclass Medical Image CBIR System Based on Classification and Clustering

Journal of Intelligent Systems

Kabbur Mahabaleshwar S.¹ 

[1] Department of Computer Science, KLE’s S. Nijalingappa College, Bangalore, India;

关键词: artificial neural network;    k-means;    clustering;    classification;    feature extraction;    hill climbing;    correlation;    contrast;    multi-texton;    energy;    homogeneity;   

DOI  :  10.1515/jisys-2016-0156

来源: DOAJ

【 摘 要 】

In this paper, we are going to present the multiclass medical image content-based image retrieval (CBIR) system based on classification and clustering. Images are segmented using hill climbing-based segmentation (HCBS) based on the extracted visual features. In the improved HCBS technique, a clustering that is based on kernel-based fuzzy C-means is employed. In the next step, features like color, texture, edge density, region area, and visual words from the segmented images are extracted. The visual word can be extracted by using the clustering techniques. This visual word represents the uniqueness of the medical image, and it is used for better classification. Then, the image can be classified by using an optimal classifier artificial neural network based on the firefly algorithm. This classification leads to filtering out the irrelevant images from the database and reduces the search space for further retrieval process. In the second stage, the relevant images are extracted from the reduced database based on the similarity measure. The proposed CBIR technique is assessed by querying different images, and the retrieval efficiency is estimated by determining the precision-recall values for the retrieval results.

【 授权许可】

Unknown