Journal of Medical Physics
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ORIGINAL ARTICLE
Year : 2008  |  Volume : 33  |  Issue : 3  |  Page : 119-126

Segmentation and classification of medical images using texture-primitive features: Application of BAM-type artificial neural network


1 School of Biomedical Engineering, Institute of Technology, Banaras Hindu University, Varanasi (UP), India
2 Department of Computer Engineering, Institute of Technology, Banaras Hindu University, Varanasi (UP), India
3 Department of Radiotherapy and Radiation Medicine, Institute of Medical Sciences, Banaras Hindu University, Varanasi (UP), India

Correspondence Address:
Neeraj Sharma
School of Biomedical Engineering, Institute of Technology, Banaras Hindu University, Varanasi (UP) - 221 005
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0971-6203.42763

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The objective of developing this software is to achieve auto-segmentation and tissue characterization. Therefore, the present algorithm has been designed and developed for analysis of medical images based on hybridization of syntactic and statistical approaches, using artificial neural network (ANN). This algorithm performs segmentation and classification as is done in human vision system, which recognizes objects; perceives depth; identifies different textures, curved surfaces, or a surface inclination by texture information and brightness. The analysis of medical image is directly based on four steps: 1) image filtering, 2) segmentation, 3) feature extraction, and 4) analysis of extracted features by pattern recognition system or classifier. In this paper, an attempt has been made to present an approach for soft tissue characterization utilizing texture-primitive features with ANN as segmentation and classifier tool. The present approach directly combines second, third, and fourth steps into one algorithm. This is a semisupervised approach in which supervision is involved only at the level of defining texture-primitive cell; afterwards, algorithm itself scans the whole image and performs the segmentation and classification in unsupervised mode. The algorithm was first tested on Markov textures, and the success rate achieved in classification was 100%; further, the algorithm was able to give results on the test images impregnated with distorted Markov texture cell. In addition to this, the output also indicated the level of distortion in distorted Markov texture cell as compared to standard Markov texture cell. Finally, algorithm was applied to selected medical images for segmentation and classification. Results were in agreement with those with manual segmentation and were clinically correlated.


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