抽象的

Artificial bee colony (ABC) algorithm and fuzzy based discriminative binary descriptor for partial duplicate medical image search in health care applications

Roselin Mary Clare K, Hemalatha M

Objective: To determine the duplicate search of medical images in health care industry to improve treatment and diagnosis of Brain Tumor (BR).

Methods: Medical image duplication search was treated in the present work. Fuzzy Sigmoid Kernel (FSK)-Subspace Clustering (SC)-Edge Scale-Invariant Feature Transform algorithm (FSK-SC-ESIFT) algorithm is presented for partial duplicate search of medical images. It contains five major steps such as deblurring, discovery of steady corner points, map extraction, detection of most discriminative bin and subspace clustering. In this research method, the image deblurring is accomplished by utilizing Artificial Bee Colony (ABC) algorithm. The constant corner point discovery is carried out by Harris Corner (HC) and map extraction is accomplished by Principal Component Analysis (PCA). Then FSK Function is used to discover the most discriminative bin selection. SC is presented for quick image retrieval.

Results: The MRI brain tumor images are used for evaluation. The results are measured using Mean Average Precision (MAP) with respect to r, D and w parameters. FSK-SC-ESIFT produces higher MAP of 70% for r value of 3.5.

Conclusion: Finally, the results show that the proposed work gives greater performance compared to the previous work.