A novel computer aided diagnostic system for quantification of metabolites in brain cancer. (April 2021)

• Record Type: Journal Article
• Authors: Sanaullah, ; Javid, Muhammad Arshad; Buzdar, Saeed Ahmad
• Is Part Of: Biomedical signal processing and control. Volume 66(2021)
• Publication Date: 2021-04
• Subjects: Brain cancer -- Tumor -- Otsu's Global Thresholding (OGT) -- Magnetic resonance imaging -- Magnetic resonance spectroscopy imaging -- Computer aided diagnosis; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102401
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Cancer diagnosis is one of the challenging tasks in medical image processing. First time, we have explored a novel method that locates tumors and its sub-regions using magnetic resonance images (MRI) and magnetic resonance spectroscopy (MRS) image processing. The structural based features of cancerous cells were estimated using MRI images and MRS spectra to evaluate the quantitative analysis of metabolites. An automated computer aided diagnosis (CAD) system was developed based on morphological operations and Otsu's Global Thresholding (OGT) technique which efficiently differentiates the normal and abnormal cells of brain. In addition, multi thresholding technique was applied to delineate intertumoral and peritumoral areas such as outer edema, necrosis, and malignancy. Brain cancer tissues were categorized into eight groups such as astrocytomas, meningiomas, lymphoma, medulloblastoma, gliomas, neoplasm, metastasis, and abscess. The proposed CAD system extracts tumor regions by inspecting them with fast and interactive morphological selection-based region analysis techniques. The segmentation of cancerous tissue was compared with MRSI to assess and extract the abnormal tissue region using textural based features. A comprehensive analysis of metabolites ratio presented the high diagnostic accuracy in high- and low-grade tumor. Experimental results of the proposed CAD system achieved 100% sensitivity, 94.73% specificity and 97.14% accuracy performance parameters in brain tumors classification which demonstrated the efficacy of cancer cells segmentation with metabolites spectra. The performance of the proposed CAD system was also compared with radiologists reports in order to use as medical diagnostic tool.