An Efficient Computer-Aided Diagnosis System for the Analysis of DICOM Volumetric Images

  • Qoseen Zahra Department of Information Technology, Government College University, Faisalabad, Pakistan.
  • Muhammad Sheraz Arshad Malik Department of Information Technology, Government College University, Faisalabad, Pakistan.
  • Naila Batool Department of Information Technology, Government College University, Faisalabad, Pakistan.


Medical images are an important source of diagnosis. The brain of human analysis is now an advanced field of research for computer scientists and biomedical physicians. Services provided by the healthcare units usually vary, the quality of treatment provided in the urban and rural generally not same. Unavailability of medical equipment and services can have serious consequences in patient disease diagnosis and treatment. In this context, we developed. MRI (Magnetic Resonance Imaging) based CAD (Computer Aided Diagnosis) system which takes MRI as input and detects abnormal tissues (Tumors). MRI is the safe and well reputed imaging methodology for prediction of tumors. MRI modality assists the medical team in diagnosis and proper treatment plan (Medication/Surgery) of different types of abnormalities in the soft tissues of the human body. This paper proposes a framework for brain cancer detection and classification. The tumor is segmented using a semi-automatic segmentation algorithm in which the threshold values selection for head and cancer regions are premeditated automatically. Segmented tumors are further sectioned into malignant and benign using SVM (Support Vector Machine) classifier. Detailed experimental work indicates that our proposed CAD system achieves higher accuracy for the analysis of brain MRI analysis.

Jul 1, 2019
How to Cite
ZAHRA, Qoseen; ARSHAD MALIK, Muhammad Sheraz; BATOOL, Naila. An Efficient Computer-Aided Diagnosis System for the Analysis of DICOM Volumetric Images. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 38, n. 3, p. 835-850, july 2019. ISSN 2413-7219. Available at: <>. Date accessed: 06 dec. 2019.
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License