Dnyanamay

Open Access Open Access  Restricted Access Subscription Access

Computer-Aided Diagnosis in Brain MR Imaging-A Step Towards Automatic Lesion Interpretation


Affiliations
1 Department of Electronics and Telecommunication, PES's Modern College of Engineering, Pune - 5., India
 

The ever-increasing stream of images and other information makes heavy demands on the radiologists. Magnetic Resonance Imaging (MRI), because of its superior soft tissue differentiation characteristics along with high spatial resolution and contrast is proved to be an important tool in the clinical and surgical environment. In this patient is not exposed to any harmful ionizing radiations. Brain MRIs may be used to assess disorders such as HIV infection of the brain, stroke, head injury, coma, Alzheimer disease, tumors and multiple sclerosis. Radiologists examine the Brain MR Images and based on visual interpretation of the films along with pathological reports, identify the presence of lesions. The large volume of MRI data to be analyzed with limited number of radiologists is labor intensive and costly task which may lead to inaccuracy in interpretation when the number of cases is more. This demands a need for automated systems for analysis and classification of different medical images. Computer assistance has already proved its value and will undoubtedly play a greater role in the future.

Keywords

Magnetic Resonance Imaging (MRI), Computer-Aided Diagnosis (CAD).
User
Notifications
Font Size

  • Kunio Doi, “Diagnostic imaging over the last 50 years: research - and development in medical imaging science and Technology” MPhys. Med. Biol. 51 (2006) R5–R27.

  • Maryellen L Giger, “Computer-aided Diagnosis in Medical Imaging – A New Era in Image Interpretation”, BUINESS BRIEFING: NEXT– GENERATION HEALTH CARE.

  • G. Newstead, L. Arbash Meinel, “Computeraided visualization and Analysis (CAVA) research system for breast cancer detection and diagnosis”, MEDICAMUNDI 52/1 2008/07.

  • Renske de Boer, Henri A. Vrooman, Fedde van der Lijn, Meike W. Vernooij, M. Arfan Ikram, Aad van der Lugt, Monique M.B. Breteler, Wiro J. Niessen, “White matter lesion extension to automatic brain tissue segmentation on MRI”, NeuroImage 45 (2009) 1151–1161.

  • P. Georgiadis et al. “Enhancing the discrimination accuracy between etastases, gliomas and meningiomas on brain MRI by volumetric extural features and ensemble pattern recognition methods” ELSEVIER, Magnetic Resonance Imaging, Volume 27 ,pp. 120–130, 2009.

  • R. Kirsi Juottonen, Mikko P. Laakso, Kaarina Partanen, and Hilkkan Soininen,“Comparative MR Analysis of the Entorhinal Cortex and Hippocampus in DiagnosingAlzheimer Disease’’, Neuroradiology 20:139–144, January 1999.

  • Multiple Sclerosis International Federation 2013, “Atlas of MS 2013, Mapping Multiple sclerosis around the world” (2002).

  • Ayelet Akselrod-Ballin,”Automatic Segmentation and Classification of Multiple Sclerosis in Multichannel MRI” IEEE transactions on biomedical engineering, vol. 56, no. 10, October 2009.

  • Multiple Sclerosis International Federation 2013, “Atlas of MS 2013, Mapping Multiple sclerosis around the world”.


Abstract Views: 139

PDF Views: 92




  • Computer-Aided Diagnosis in Brain MR Imaging-A Step Towards Automatic Lesion Interpretation

Abstract Views: 139  |  PDF Views: 92

Authors

Rupali Kamathe
Department of Electronics and Telecommunication, PES's Modern College of Engineering, Pune - 5., India
Kalyani Joshi
Department of Electronics and Telecommunication, PES's Modern College of Engineering, Pune - 5., India

Abstract


The ever-increasing stream of images and other information makes heavy demands on the radiologists. Magnetic Resonance Imaging (MRI), because of its superior soft tissue differentiation characteristics along with high spatial resolution and contrast is proved to be an important tool in the clinical and surgical environment. In this patient is not exposed to any harmful ionizing radiations. Brain MRIs may be used to assess disorders such as HIV infection of the brain, stroke, head injury, coma, Alzheimer disease, tumors and multiple sclerosis. Radiologists examine the Brain MR Images and based on visual interpretation of the films along with pathological reports, identify the presence of lesions. The large volume of MRI data to be analyzed with limited number of radiologists is labor intensive and costly task which may lead to inaccuracy in interpretation when the number of cases is more. This demands a need for automated systems for analysis and classification of different medical images. Computer assistance has already proved its value and will undoubtedly play a greater role in the future.

Keywords


Magnetic Resonance Imaging (MRI), Computer-Aided Diagnosis (CAD).

References