Superpixel algorithms oversegment an image by grouping pixels with similar local features such as spatial position, gray level intensity, color, and texture. Superpixels provide visually significant regions and avoid a large number of redundant information to reduce dimensionality and complexity for subsequent image processing tasks. However, superpixel algorithms decrease performance in images with high-frequency contrast variations in regions of uniform texture. Moreover, most state-of-the-art methods use only basic pixel information -spatial and color-, getting superpixels with low regularity, boundary smoothness and adherence. The proposed algorithm adds texture information to the common superpixel representation. This information is obtained with the Hermite Transform, which extracts local texture features in terms of Gaussian derivatives. A local iterative clustering with adaptive feature weights generates superpixels preserving boundary adherence, smoothness, regularity, and compactness. A feature adjustment stage is applied to improve algorithm performance. We tested our algorithm on Berkeley Segmentation Dataset and evaluated it with standard superpixel metrics. We also demonstrate the usefulness and adaptability of our proposal in medical image application.
Segmentation of knee cartilage has been useful for opportune diagnosis and treatment of osteoarthritis (OA). This paper presents a semiautomatic segmentation technique based on Active Shape Models (ASM) combined with Local Binary Patterns (LBP) and its approaches to describe the surrounding texture of femoral cartilage. The proposed technique is tested on a 16-image database of different patients and it is validated through Leave- One-Out method. We compare different segmentation techniques: ASM-LBP, ASM-medianLBP, and ASM proposed by Cootes. The ASM-LBP approaches are tested with different ratios to decide which of them describes the cartilage texture better. The results show that ASM-medianLBP has better performance than ASM-LBP and ASM. Furthermore, we add a routine which improves the robustness versus two principal problems: oversegmentation and initialization.
In this paper, we propose to take advantage from the contrast characteristics of our magnetic resonance images in order to improve the performance of Active Shape Models (ASM) applied to knee cartilage segmentation, since ASM depends directly of the contrast between objects. We realize an image fusion-based contrast enhancement between slices from magnetic resonance image volumes, then, we test the ASM algorithm with contrast enhancement images and compare results with ASM without contrast enhancement. The results are very clear, the ASM with contrast enhancement has a better performance and consistence, and we validate this results using Dice coefficient and Hausdorff distance. Moreover, we validate contrast enhancement to assure that really we are improving the contrast image.