Traditional marbling meat evaluation is a tedious, repetitive, costly and time-consuming task performed by panellists. Alternatively, we have Computer Vision Systems (CVS) to mitigate these problems. However, most of CVS are restricted to specific environments, configurations or muscle types, and marbling scores are settled for a particular marbling meat standard. In this context, we developed a CVS for meat marbling grading, which is flexible to different muscle colour contrasts and grading standards. Essentially, the proposed method segments an image pre-processed by illumination normalisation and contrast enhancement, analyses visible intramuscular fat pixels and attributes a score based on a desired meat standard defined in the learning step. Learning approach is an instance-based system making use of k-Nearest Neighbours algorithm (k-NN) to attribute a score from segmentation results. The algorithm classifies the new samples based on scores assigned by panellists. We investigated the optimal number of samples for modelling, focusing on the smallest number leading to acceptable accuracy, and considering two different animal species: bovine and 1 swine. The CVS led to accuracy values equal to 81.59% (bovine) and to 76.14% (swine), using only three samples for each marbling score.

Misleading diagnosis of skin diseases may result in complications during the healing process. Skin images provide an important contribution to medical staff on storing and exchanging information to try preventing misdiagnosis. For such, image segmentation process may benefit from use of machine learning techniques, increasing simplicity of procedure, reducing computational costs and improving the diagnosis. This paper presents a comparison among different paradigms of machine learning to validate the segmentation of medical images of lower members ulcers, this segmentation allows wound pattern recognition to determinate injury region aiming at reducing the subjectivity of human evaluation.

This paper presents a seed finding method for region growing segmentation approach using color channel energy in image regions. Instead of using the RGB system separated for each pixel, the proposal uses the energy on each color channel to improve the range of the possible values, then creates a more specific seed to detail different regions. Region size used to calculate energy was adjusted to verify the proposed method. Images used were real wound photos, taken from patients undergoing treatment at the university hospital. Results showed that energy on regions presents enough information to segment, leading to a high percentage of matching with experts marks.

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In this study, we developed a robust automatic computer vision system for marbling meat segmentation. Our approach can segment intramuscular fat from meat samples using images acquired with different quality devices in an illumination varying environment, where there was external ambient light and artificial light; thus, professionals can apply this method without specialized knowledge in terms of image treatment or equipment, as well as without disruption to normal procedures, thereby obtaining a robust solution. The proposed approach for marbling segmentation is based on data clustering and dynamic thresholding. Experiments were performed using two datasets that comprised 82 images of 41 longissimus dorsi muscles acquired by different sampling devices. The experimental results showed that the computer vision system performed well with over 98% accuracy and a low number of false positives, regardless of the acquisition device employed.