A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

This paper aims to make a review on the current segmentation algorithms used for medical images. Algorithms are classified according to their principal methodologies, namely the ones based on thresholds, the ones based on clustering techniques and the ones based on deformable models. The last type is focused on due to the intensive investigations into the deformable models that have been done in the last few decades. Typical algorithms of each type are discussed and the main ideas, application fields, advantages and disadvantages of each type are summarised. Experiments that apply these algorithms to segment the organs and tissues of the female pelvic cavity are presented to further illustrate their distinct characteristics. In the end, the main guidelines that should be considered for designing the segmentation algorithms of the pelvic cavity are proposed

Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach

Crohn's disease (CD) diagnosis is a tremendouslyserious health problem due to its ultimately effecton the gastrointestinal tract that leads to the need of complexmedical assistance. In this study, the backpropagationneural network fuzzy classifier and a neuro-fuzzy modelare combined for diagnosing the CD. Factor analysis isused for data dimension reduction. The effect on the systemperformance has been investigated when using fuzzypartitioning and dimension reduction. Additionally, furthercomparison is done between the different levels of thefuzzy partition to reach the optimal performance accuracylevel. The performance evaluation of the proposed systemis estimated using the classification accuracy and othermetrics. The experimental results revealed that the classificationwith level-8 partitioning provides a classificationaccuracy of 97.67 %, with a sensitivity and specificity of96.07 and 100 %, respectively