Segmentation of articular cartilage and early osteoarthritis based on the fuzzy soft thresholding approach driven by modified evolutionary ABC optimization and local statistical aggregation

Articular cartilage assessment, with the aim of the cartilage loss identification, is a crucial task for the clinical practice of orthopedics. Conventional software (SW) instruments allow for just a visualization of the knee structure, without post processing, offering objective cartilage modeling. In this paper, we propose the multiregional segmentation method, having ambitions to bring a mathematical model reflecting the physiological cartilage morphological structure and spots, corresponding with the early cartilage loss, which is poorly recognizable by the naked eye from magnetic resonance imaging (MRI). The proposed segmentation model is composed from two pixel's classification parts. Firstly, the image histogram is decomposed by using a sequence of the triangular fuzzy membership functions, when their localization is driven by the modified artificial bee colony (ABC) optimization algorithm, utilizing a random sequence of considered solutions based on the real cartilage features. In the second part of the segmentation model, the original pixel's membership in a respective segmentation class may be modified by using the local statistical aggregation, taking into account the spatial relationships regarding adjacent pixels. By this way, the image noise and artefacts, which are commonly presented in the MR images, may be identified and eliminated. This fact makes the model robust and sensitive with regards to distorting signals. We analyzed the proposed model on the 2D spatial MR image records. We show different MR clinical cases for the articular cartilage segmentation, with identification of the cartilage loss. In the final part of the analysis, we compared our model performance against the selected conventional methods in application on the MR image records being corrupted by additive image noise.Web of Science117art. no. 86

Anatomy Segmentation of Breast Ultrasound images

Breast cancer is one of the most common cancers in women, affecting hundreds of women. Even though the detection of cancer has been largely studied, the decision of which strategy to take concerning oncoplastic surgery still relies almost exclusively on the surgeon's perception of post-surgical aesthetic result, which sometime leads to unsatisfactory outcomes. In order to empower the patients on the joint decision process there needs to exist a better communication between the parts. This can be achieved by developing medical grade 3D models of the breast and explaining better the surgical options and their results. In order to obtain such models, some effort has been made concerning multi-modality radiological imaging combination. This line of research has yet to mature. In turn, the modality alignment requires accurate landmarks to be produced. 2D Ultrasound imaging has not been sufficiently studied for multimodal registration due to the image characteristics and thus, landmark segmentation is of utmost importance. This task can be challenging since US data presents high specular noise levels and the presence of some tissues alters the perception of other tissues. Objectives: ● Study and evaluation of different techniques for anatomical landmark segmentation, such as Skin, Fat and Glandular tissue, Lesions (masses and cysts), Pectoral muscle; ● Development of Ultrasound segmentation methods for acquiring landmarks; ● Evaluation of the developed methods with manual annotations and comparison of results with the current algorithm alternatives.Breast cancer is one of the most common cancers in women, affecting hundreds of women. Even though the detection of cancer has been largely studied, the decision of which strategy to take concerning oncoplastic surgery still relies almost exclusively on the surgeon's perception of post-surgical aesthetic result, which sometime leads to unsatisfactory outcomes. In order to empower the patients on the joint decision process there needs to exist a better communication between the parts. This can be achieved by developing medical grade 3D models of the breast and explaining better the surgical options and their results. In order to obtain such models, some effort has been made concerning multi-modality radiological imaging combination. This line of research has yet to mature. In turn, the modality alignment requires accurate landmarks to be produced. 2D Ultrasound imaging has not been sufficiently studied for multimodal registration due to the image characteristics and thus, landmark segmentation is of utmost importance. This task can be challenging since US data presents high specular noise levels and the presence of some tissues alters the perception of other tissues. Objectives: ● Study and evaluation of different techniques for anatomical landmark segmentation, such as Skin, Fat and Glandular tissue, Lesions (masses and cysts), Pectoral muscle; ● Development of Ultrasound segmentation methods for acquiring landmarks; ● Evaluation of the developed methods with manual annotations and comparison of results with the current algorithm alternatives

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Neutro-Connectedness Theory, Algorithms and Applications

Connectedness is an important topological property and has been widely studied in digital topology. However, three main challenges exist in applying connectedness to solve real world problems: (1) the definitions of connectedness based on the classic and fuzzy logic cannot model the “hidden factors” that could influence our decision-making; (2) these definitions are too general to be applied to solve complex problem; and (4) many measurements of connectedness are heavily dependent on the shape (spatial distribution of vertices) of the graph and violate the intuitive idea of connectedness. This research focused on solving these challenges by redesigning the connectedness theory, developing fast algorithms for connectedness computation, and applying the newly proposed theory and algorithms to solve challenges in real problems. The newly proposed Neutro-Connectedness (NC) generalizes the conventional definitions of connectedness and can model uncertainty and describe the part and the whole relationship. By applying the dynamic programming strategy, a fast algorithm was proposed to calculate NC for general dataset. It is not just calculating NC map, and the output NC forest can discover a dataset’s topological structure regarding connectedness. In the first application, interactive image segmentation, two approaches were proposed to solve the two most difficult challenges: user interaction-dependence and intense interaction. The first approach, named NC-Cut, models global topologic property among image regions and reduces the dependence of segmentation performance on the appearance models generated by user interactions. It is less sensitive to the initial region of interest (ROI) than four state-of-the-art ROI-based methods. The second approach, named EISeg, provides user with visual clues to guide the interacting process based on NC. It reduces user interaction greatly by guiding user to where interacting can produce the best segmentation results. In the second application, NC was utilized to solve the challenge of weak boundary problem in breast ultrasound image segmentation. The approach can model the indeterminacy resulted from weak boundaries better than fuzzy connectedness, and achieved more accurate and robust result on our dataset with 131 breast tumor cases

Image Segmentation Techniques: A Survey

Segmenting an image utilizing diverse strategies is the primary technique of Image Processing. The technique is broadly utilized in clinical image handling, face acknowledgment, walker location, and so on. Various objects in an image can be recognized using image segmentation methods. Researchers have come up with various image segmentation methods for effective analysis. This paper presents a survey and sums up the designs process of essential image segmentation methods broadly utilized with their advantages and weaknesses