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Mandal, Mrinal
- Computer-Aided Detection of Acinar Shadows in Chest Radiographs
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Authors
Affiliations
1 Department of Electrical and Computer Engineering, University of Alberta, CA
2 Department of Computing Science, University of Alberta, CA
3 Department of Medicine, University of Alberta, CA
1 Department of Electrical and Computer Engineering, University of Alberta, CA
2 Department of Computing Science, University of Alberta, CA
3 Department of Medicine, University of Alberta, CA
Source
ICTACT Journal on Image and Video Processing, Vol 3, No 4 (2013), Pagination: 593-604Abstract
Despite the technological advances in medical diagnosis, accurate detection of infectious tuberculosis (TB) still poses challenges due to complex image features and thus infectious TB continues to be a public health problem of global proportions. Currently, the detection of TB is mainly conducted visually by radiologists examining chest radiographs (CXRs). To reduce the backlog of CXR examination and provide more precise quantitative assessment, computer-aided detection (CAD) systems for potential lung lesions have been increasingly adopted and commercialized for clinical practice. CADs work as supporting tools to alert radiologists on suspected features that could have easily been neglected. In this paper, an effective CAD system aimed for acinar shadow regions detection in CXRs is proposed. This system exploits textural and photometric features analysis techniques which include local binary pattern (LBP), grey level co-occurrence matrix (GLCM) and histogram of oriented gradients (HOG) to analyze target regions in CXRs. Classification of acinar shadows using Adaboost is then deployed to verify the performance of a combination of these techniques. Comparative study in different image databases shows that the proposed CAD system delivers consistent high accuracy in detecting acinar shadows.Keywords
Textural and Photometric Classification, Computer-Aided Detection (CAD), Tuberculosis (TB).
- Automated Corpus Callosum Segmentation in Midsagittal Brain MR Images
Abstract Views :221 |
PDF Views:7
Authors
Affiliations
1 Department of Electrical and Computer Engineering, University of Alberta, CA
2 Department of Medicine, University of Alberta, CA
1 Department of Electrical and Computer Engineering, University of Alberta, CA
2 Department of Medicine, University of Alberta, CA
Source
ICTACT Journal on Image and Video Processing, Vol 8, No 1 (2017), Pagination: 1554-1565Abstract
Corpus Callosum (CC) is an important white-matter structure in the human brain. Magnetic resonance imaging (MRI) is a non-invasive medical imaging technique that provides high resolution images for the structures. Segmentation is an important step in medical image analysis. This paper proposes a fully automated technique for segmentation of CC on the midsagittal slice of T1-weighted brain MR images. The proposed technique consists of three modules. First it clusters all homogenous regions in the image with an adaptive mean shift (AMS) technique. The automatic CC contour initialization (ACI) is achieved using the region analysis, template matching and location analysis, thus identify the CC region. Finally, the boundary of recognized CC region is used as the initial contour in the Geometric Active Contour (GAC) model, and is evolved to obtain the final segmentation result of CC. Experimental results demonstrate that the proposed AMS-ACI technique is able to provide accurate initial CC contour, and the proposed AMS-ACI-GAC technique overcomes the problem of user-guided initialization in existing GAC techniques, and provides a reliable and accurate performance in CC segmentation.Keywords
Adaptive Mean Shift Clustering, Automated Segmentation, Corpus Callosum, Geometric Active Contour, Template Matching.References
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