Hierarchical clustering-based segmentation (HCS) aided interpretation of the DCE MR Images of the Prostate

In Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) for prostate cancer, there is early intense enhancement and rapid washout of contrast material, due to the heterogeneous and leaky characteristics of the tumour angiogenesis. These characteristics can be demonstrated by the quantitative measurement of signal enhancement with time (Time Intensity Curve). The TIC is plotted for the pixels', averaged intensity value, within a user drawn Region of Interest (ROI). The ROI, normally chosen within an area of the largest enhancement, may enclose tissues of different enhancement pattern. Hence the averaged TIC from the ROI may not represent the actual characteristics of the enclosed tissue of interest. Hierarchical Clustering-based Segmentation (HCS) is an approach to Computer Aided Monitoring (CAM) that generates a hierarchy of segmentation results to highlight the varied dissimilarities in images. As a diagnostic aid for the analysis of DCE-MR image data, the process starts with the HCS process applied to all the DCE-MR temporal frames of a slice. HCS process output provides heat map images based on the normalised average pixel value of the various dissimilar regions. TIC of the contrast wash-in, wash-out process are then plotted for suspicious regions confirmed by the user. In this paper we have demonstrated how the HCS process as asemi-quantitative analytical tool to analyse the DCE MR images of the Prostate complements the radiologist's interpretation of DCE MR images

Biomedical Image Processing and Classification

Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture

Hierarchical clustering-based segmentation (HCS) aided interpretation of the DCE MR Images of the Prostate

Abstract In Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) for prostate cancer, there is early intense enhancement and rapid washout of contrast material, due to the heterogeneous and leaky characteristics of the tumour angiogenesis. These characteristics can be demonstrated by the quantitative measurement of signal enhancement with time (Time Intensity Curve). The TIC is plotted for the pixels', averaged intensity value, within a user drawn Region of Interest (ROI). The ROI, normally chosen within an area of the largest enhancement, may enclose tissues of different enhancement pattern. Hence the averaged TIC from the ROI may not represent the actual characteristics of the enclosed tissue of interest. Hierarchical Clustering-based Segmentation (HCS) is an approach to Computer Aided Monitoring (CAM) that generates a hierarchy of segmentation results to highlight the varied dissimilarities in images. As a diagnostic aid for the analysis of DCE-MR image data, the process starts with the HCS process applied to all the DCE-MR temporal frames of a slice. HCS process output provides heat map images based on the normalised average pixel value of the various dissimilar regions. TIC of the contrast wash-in, wash-out process are then plotted for suspicious regions confirmed by the user. In this paper we have demonstrated how the HCS process as a semiquantitative analytical tool to analyse the DCE MR images of the Prostate complements the radiologist's interpretation of DCE MR images