Automatic segmentation of plantar thermograms using adaptive C means technique

Diabetic foot ulcer (DFU) is one of the major concern of diabetes and it is rapidly increasing, in worst case scenario this may lead to amputation. The DFU can be avoided by the early detection and proper diagnosis. Many of the studies carried out highlights that, thermography is the most useful technique to measure the changes in the temperature of plantar surface and alerts to indicate the risk associated with DFU. The distribution of temperature does not have a fixed pattern across the patients, hence it makes the difficulty in measuring the appropriate changes. This gap will provide a scope to improve the analysis technique so as to measure the plantar surface temperature effectively and identify any abnormal changes. In this paper, the segmentation algorithm namely adaptive C means (ACM) for the image segmentation is discussed. ACM is based on the spatial information and this method includes the two stage implementation. In the first stage, nonlocal spatial information is added and in the second stage, spatial shape information is used in order to refine the constraint of local spatial. Outcome of the proposed method shows that ACM is very much effective and it outperforms the other existing methods

CatSIM: A Categorical Image Similarity Metric

We introduce CatSIM, a new similarity metric for binary and multinary two- and three-dimensional images and volumes. CatSIM uses a structural similarity image quality paradigm and is robust to small perturbations in location so that structures in similar, but not entirely overlapping, regions of two images are rated higher than using simple matching. The metric can also compare arbitrary regions inside images. CatSIM is evaluated on artificial data sets, image quality assessment surveys and two imaging applicationsComment: 17 pages, 16 figures, 10 table

A formal method for selecting evaluation metrics for image segmentation

Evaluating the quality of segmentations is an important process in image processing, especially in the medical domain. Many evaluation metrics have been used in evaluating segmentation. There exists no formal way to choose the most suitable metric(s) for a particular segmentation task and/or particular data. In this pa- per we propose a formal method for choosing the most suitable metrics for evaluating the quality of segmenta- tions with respect to ground truth segmentations. The proposed method depends on measuring the bias of metrics towards/against the properties of the the seg- mentations being evaluated. We firstly demonstrate how metrics can have bias towards/against particular properties and then we propose a general method for ranking metrics according to their overall bias. We fi- nally demonstrate for 3D medical image segmentations that ranking produced using metrics with low overall bias strongly correlate with manual rankings done by an expert