Abstract
Segmentation of noisy and low-contrast images remains one of the most challenging and difficult tasks, especially in the context of medical imaging. In this work, we propose an extension of the Active Shape Models (ASM) which is based on a priori knowledge about the shape and the deformation modes of the studied Region(s) of Interest (ROI). The main contribution of the proposed extension resides in the integration of a statistical directional relationship within the ASM, which is learned during a training phase. In particular, in order to force the active contour to move towards points in space that satisfy the spatial relationship, we propose a fuzzy directional constraint that allows a more robust localization of ROI. In fact, the learned a priori knowledge has been modeled using fuzzy logic in order to model uncertainty and ambiguity of the spatial representation. Realized tests on scintigraphic and MRI images proved the performance of the proposed model for the detection of multiple objects of interest in noisy and low-contrast images, even when real contours are ill-defined.
Keywords: Active shape models, a priori knowledge, fuzzy logic, medical image segmentation, spatial relationships.
Graphical Abstract
Call for Papers in Thematic Issues
Intelligent Information Retrieval for Multispectral and Hyperspectral Imaging
Multispectral and hyperspectral imaging is an interdisciplinary research area, widely focuses on spectral, spatial, and temporal data. It offers a plethora of opportunities to efficiently analyze the vast area of the earth's surface. However, there are numerous factors such as dimensionality and size of the hyperspectral data, lesser training samples, ...read more
Machine and Deep Learning in Medical Image Diagnosis
Medical Image Processing regards a set of methodologies that have been developed over recent years with the purpose of improving medical image quality, improving medical data visualization, understanding, and assisting medical diagnosis, and so on. The application of machine and deep learning methods in sensing and imaging can potentially have ...read more
Quantum Machine Learning for Medical Data and Imaging
Quantum computing has promised a significant speedup in certain computationally intensive tasks that are intractable on classical computers. Researchers in quantum machine learning, such as those working in computer vision, image processing, biomedical analysis, and related topics, may play an important role in comprehending and working on complicated medical data, ...read more
23
1
1
1