Pub Date : 2012-03-01DOI: 10.1109/ICPRIME.2012.6208367
R. Manavalan, K. Thangavel
Ultrasound imaging is most suitable method for early detection of prostate cancer. It is very difficult to distinguish benign and malignant in the early stage of cancer. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based classification system can provide a second opinion to the radiologists. Generally objects are described in terms of a set of measurable features in pattern recognition. Feature selection is a process of selecting the most wanted or dominating features set from the original features set in order to reduce the cost of data visualization and increasing classification efficiency and accuracy. The Region of Interest (ROI) is identified from the Transrectal Ultrasound (TRUS) images using DBSCAN clustering with morphological operators. Then the statistical texture features are extracted from the ROIs. Rough Set based Quick Reduct (QR) and Evolutionary based Ant Colony Optimization (ACO) is studied. In this paper, Hybridization of Rough Set based QR and ACO is proposed for dimensionality reduction. The selected features may have the best discriminatory power for classifying prostate cancer based on TRUS images. Support Vector Machine (SVM) is tailored for evaluation of the proposed feature selection methods through classification. Then, the comparative analysis is performed among these methods. Experimental results show that the proposed method QR-ACO produces significant results. Number of features selected using QR-ACO algorithm is minimal, and is successful and has high detection accuracy.
{"title":"Quick Reduct-ACO based feature selection for TRUS prostate cancer image classification","authors":"R. Manavalan, K. Thangavel","doi":"10.1109/ICPRIME.2012.6208367","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208367","url":null,"abstract":"Ultrasound imaging is most suitable method for early detection of prostate cancer. It is very difficult to distinguish benign and malignant in the early stage of cancer. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based classification system can provide a second opinion to the radiologists. Generally objects are described in terms of a set of measurable features in pattern recognition. Feature selection is a process of selecting the most wanted or dominating features set from the original features set in order to reduce the cost of data visualization and increasing classification efficiency and accuracy. The Region of Interest (ROI) is identified from the Transrectal Ultrasound (TRUS) images using DBSCAN clustering with morphological operators. Then the statistical texture features are extracted from the ROIs. Rough Set based Quick Reduct (QR) and Evolutionary based Ant Colony Optimization (ACO) is studied. In this paper, Hybridization of Rough Set based QR and ACO is proposed for dimensionality reduction. The selected features may have the best discriminatory power for classifying prostate cancer based on TRUS images. Support Vector Machine (SVM) is tailored for evaluation of the proposed feature selection methods through classification. Then, the comparative analysis is performed among these methods. Experimental results show that the proposed method QR-ACO produces significant results. Number of features selected using QR-ACO algorithm is minimal, and is successful and has high detection accuracy.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125819643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-03-01DOI: 10.1109/ICPRIME.2012.6208363
J. Ganesh Sivakumar, K. Thangavel, P. Saravanan
Medical imaging devices are used to scan different organs of human being and used in different stages of analysis. Magnetic Resonance Image (MRI), Computer Tomography (CT), Ultrasound and X-Ray are some of the imaging techniques adopted for acquiring images to diagnose most of the diseases. The main aim of this study is to improve the quality of Computed Radiography (CR) medical images. Denoising with edge preservation is very important in CR X-Ray imaging. Noise reduction should be a great concern in order not to lose detailed spatial information for perfect and optimal diagnosis of diseases. Computing techniques also need to be taken care of since the digital format of the medical images is comprised with large sized matrices. In this study, firstly, we compared a series of filtering techniques using Wiener filtering method to remove the Poisson noise from CR X-Ray human Skull images. Secondly, Contrast Enhancement was performed by using Histogram Equalization and intensity value adjustment with limits points. The main aim of this work is to improve the visual quality of CR X-Ray human skull images and enhance the subtle details such as edges and nodules, which are with low contrast white circular objects. The performance of the proposed method is analyzed using Means Square Error (MSE) and Peak Signal Noise Ratio (PSNR) measures. Experimental results show that Wiener Filtering method effectively reduce the Poisson noise from CR X-Ray of a human Skull image. Finally the study is concluded with future implications for research areas.
{"title":"Computed radiography skull image enhancement using Wiener filter","authors":"J. Ganesh Sivakumar, K. Thangavel, P. Saravanan","doi":"10.1109/ICPRIME.2012.6208363","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208363","url":null,"abstract":"Medical imaging devices are used to scan different organs of human being and used in different stages of analysis. Magnetic Resonance Image (MRI), Computer Tomography (CT), Ultrasound and X-Ray are some of the imaging techniques adopted for acquiring images to diagnose most of the diseases. The main aim of this study is to improve the quality of Computed Radiography (CR) medical images. Denoising with edge preservation is very important in CR X-Ray imaging. Noise reduction should be a great concern in order not to lose detailed spatial information for perfect and optimal diagnosis of diseases. Computing techniques also need to be taken care of since the digital format of the medical images is comprised with large sized matrices. In this study, firstly, we compared a series of filtering techniques using Wiener filtering method to remove the Poisson noise from CR X-Ray human Skull images. Secondly, Contrast Enhancement was performed by using Histogram Equalization and intensity value adjustment with limits points. The main aim of this work is to improve the visual quality of CR X-Ray human skull images and enhance the subtle details such as edges and nodules, which are with low contrast white circular objects. The performance of the proposed method is analyzed using Means Square Error (MSE) and Peak Signal Noise Ratio (PSNR) measures. Experimental results show that Wiener Filtering method effectively reduce the Poisson noise from CR X-Ray of a human Skull image. Finally the study is concluded with future implications for research areas.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122196221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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