Verification of new method for automatic thickness measurement of melanocytic skin tumours by high frequency ultrasound

Histological thickness of cutaneous melanoma (CM), known as the Breslow index (pT), represents the most important prognostic factor. The objective of this study is to evaluate the reliability of automatic algorithm based on B-scan image processing of 22 MHz ultrasound (US) for measuring the thickness of CM and melanocytic nevi (MN). The thickness of CM (n = 54) and MN (n = 91) has been measured manually (mT) and automatically (aT) using an algorithm based on B-scan image processing of 22 MHz US. All melanocytic skin tumours (MST) were surgically excised and their histological thicknesses (pT) according to Breslow were evaluated. The investigated parameters were expressed as medians with interquartile range (IQR) because of their asymmetric distribution, Spearman’s correlation coefficient was determined as well. An agreement between values of mT/aT and mT/pT was evaluated by using the Bland-Altman plots. We found a good agreement of aT and mT with the moderate bias of 0.08 mm and relatively small range (95 % CI –0.01 to 0.18) in CM, accordingly 0.03 mm (95 % CI 0.00 to 0.07 mm) regarding MN. The medians of mT/pT in cases of CM and MN were 0.96 mm (IQR: 0.65-1.52) / 0.97 (IQR: 0.66-1.62) and 0.51 mm (IQR: 0.37-0.67) / 0.69 mm (IQR: 0.46-1.01) respectively. The parameters of the thickness correlated better in CM (r = 0.86) than in MN (r = 0.64) cases. The difference between manual (mT) and automatic (aT) measurements while evaluating the thickness of MST was non-significant. Therefore, automatic algorithm based on B-scan image processing of 22 MHz US is a reliable tool for measuring the thickness of MST by less experienced operators

Verification of new method for automatic thickness measurement of melanocytic skin tumours by high frequency ultrasound

Histological thickness of cutaneous melanoma (CM), known as the Breslow index (pT), represents the most important prognostic factor. The objective of this study is to evaluate the reliability of automatic algorithm based on B-scan image processing of 22 MHz ultrasound (US) for measuring the thickness of CM and melanocytic nevi (MN). The thickness of CM (n = 54) and MN (n = 91) has been measured manually (mT) and automatically (aT) using an algorithm based on B-scan image processing of 22 MHz US. All melanocytic skin tumours (MST) were surgically excised and their histological thicknesses (pT) according to Breslow were evaluated. The investigated parameters were expressed as medians with interquartile range (IQR) because of their asymmetric distribution, Spearman’s correlation coefficient was determined as well. An agreement between values of mT/aT and mT/pT was evaluated by using the Bland-Altman plots. We found a good agreement of aT and mT with the moderate bias of 0.08 mm and relatively small range (95 % CI –0.01 to 0.18) in CM, accordingly 0.03 mm (95 % CI 0.00 to 0.07 mm) regarding MN. The medians of mT/pT in cases of CM and MN were 0.96 mm (IQR: 0.65-1.52) / 0.97 (IQR: 0.66-1.62) and 0.51 mm (IQR: 0.37-0.67) / 0.69 mm (IQR: 0.46-1.01) respectively. The parameters of the thickness correlated better in CM (r = 0.86) than in MN (r = 0.64) cases. The difference between manual (mT) and automatic (aT) measurements while evaluating the thickness of MST was non-significant. Therefore, automatic algorithm based on B-scan image processing of 22 MHz US is a reliable tool for measuring the thickness of MST by less experienced operators

An Efficient Technique to Detect Visual Defects in Particleboards

This paper is concerned with the problem of image analysis based detection of local defects embedded in particleboard surfaces. Though simple, but efficient technique developed is based on the analysis of the discrete probability distribution of the image intensity values and the 2D discrete Walsh transform. Robust global features characterizing a surface texture are extracted and then analyzed by a pattern classifier. The classifier not only assigns the pattern into the quality or detective class, but also provides the certainty value attributed to the decision. A 100% correct classification accuracy was obtained when testing the technique proposed on a set of 200 images

Algorithm for automated foot detection in thermal and optical Images for temperature asymmetry analysis

Infrared thermography has been proven to be an effective non-invasive method in diabetic foot ulcer prevention, yet current image processing algorithms are inaccurate and impractical for clinical work. The aim of this study was to investigate the accuracy of our automated algorithm for feet outline detection and localization of potential inflammation regions in thermal images. Optical and thermal images were captured by a Flir OnePro camera connected with an Apple iPad Air tablet. Both thermal and optical images were merged into an edge image and used for the estimation of foot template transformations during the localization process. According to the feet template transformations, temperature maps were calculated and compared with each other to detect a set of regions exceeding the defined temperature threshold. Finally, a set of potential inflammation regions were filtered according to the blobs features to obtain the final list of inflammation regions. In this study, 168 thermal images were analyzed. The developed algorithm yielded 95.83% accuracy for foot outline detection and 94.28% accuracy for detection of the inflammation regions. The presented automated algorithm with enhanced detection accuracy can be used for developing a mobile thermal imaging system. Further studies with patients who have diabetes and are at risk of foot ulceration are needed to test the significance of our developed algorithm

A general framework for designing a fuzzy rule-based classifier

This paper presents a general framework for designing a fuzzyrule-based classifier. Structure and parameters of the classifierare evolved through a two-stage genetic search. To reduce the searchspace, the classifier structure is constrained by a tree createdusing the evolving SOM tree algorithm. Salient input variables arespecific for each fuzzy rule and are found during the genetic searchprocess. It is shown through computer simulations of four real worldproblems that a large number of rules and input variables can beeliminated from the model without deteriorating the classificationaccuracy. By contrast, the classification accuracy of unseen data isincreased due to the elimination.This paper presents a general framework for designing a fuzzyrule-based classifier. Structure and parameters of the classifierare evolved through a two-stage genetic search. To reduce the searchspace, the classifier structure is constrained by a tree createdusing the evolving SOM tree algorithm. Salient input variables arespecific for each fuzzy rule and are found during the genetic searchprocess. It is shown through computer simulations of four real worldproblems that a large number of rules and input variables can beeliminated from the model without deteriorating the classificationaccuracy. By contrast, the classification accuracy of unseen data isincreased due to the elimination