Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Breast cancer diagnosis using neural-based linear fusion strategies
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Fully automatic segmentation of coronary vessel structures in poor quality x-ray angiogram images
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Bayesian model combination and its application to cervical cancer detection
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Integration of expert knowledge and image analysis techniques for medical diagnosis
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Locating human eyes using edge and intensity information
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
Applying GCS networks to fuzzy discretized microarray data for tumour diagnosis
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Automated segmentation of the menisci from MR images
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Computer Methods and Programs in Biomedicine
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Everyday vast amount of information accumulated in medical databases. These databases include quite useful information that could be exploited to improve diagnosis of illnesses and their treatments. However, classification of this information is becoming more and more difficult. In this paper, an automatic method to diagnose the knee meniscus tears from MR medical images is presented. This proposed system uses histogram based method with edge detection filtering and statistical segmentation based methods to locate meniscus at knee joint. A template matching technique is also employed to extract the meniscus. Finally, the meniscus area is analyzed to detect the meniscus tears automatically. Accurate segmentation of the statistical pattern requires a technique that eliminates background effects. Hence, the density distributions of the statistical patterns on images with varying background are corrected. Here, the statistical segmentation method also extracts a representing image of the statistical patterns such as bone and uses the image to enhance the segmentation. Performance of this method is examined on MR images in varying qualities. The results show that our method is quite successful in segmentation of knee bones and diagnosis of the meniscus tears. This system has achieved accuracy about 93% in the diagnosis of meniscus tears on MR images.