An intelligent assistive algorithm for bone tumor detection from human X-Ray images based on binary Blob analysis

被引：0

作者：

Bharodiya A.K. ^{[1
]}

Gonsai A.M. ^{[2
]}

机构：

[1] BCA Department, UCCC & SPBCBA & SDHG College of BCA & I.T., Udhna, Gujarat, Surat

[2] Department of Computer Science, Saurashtra University, Gujarat, Rajkot

来源：

International Journal of Information Technology | 2022年 / 14卷 / 3期

关键词：

Blob; Bone tumor; Gaussian filter; Orthopaedics; ROI; Segmentation; X-ray;

D O I：

10.1007/s41870-020-00539-0

中图分类号：

学科分类号：

摘要：

Image segmentation is an essential phase of medical image processing. Orthopaedics practitioners suggest X-Ray imaging to detect bone related diseases of the patient. Due to increase in the number of bone cancers, bone tumor detection and its segmentation from X-Ray image has become thirst area of research in the medical image analysis. In this research paper, an intelligent assistive algorithm has been proposed, which is called BTDBB to identify bone tumor from human being’s X-Ray images. The proposed algorithm accepts human arm X-Ray images, converts into grayscale, do Gaussian filtering to remove noise and enhancement, Segmentation to divide image into different parts based on threshold, detection of ROI using binary blob pattern analysis, crop image to retain ROI only, measurement of tumor size and finally, detection of bone tumor. The algorithm is implemented in Scilab 5.5.2 open source image processing software using 109 X-Ray images as dataset, out of which 82 images were bone tumor infected. We have compared our proposed algorithm with existing algorithms/methods for performance evaluation using different types of accuracies as evaluation metrics. Further, we have found that proposed algorithm yields an average accuracy of 99.12% and hence, it is superior in performance over existing selected algorithms/methods based on selected parameters. © 2020, Bharati Vidyapeeth's Institute of Computer Applications and Management.

引用

页码：1467 / 1473

页数：6

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