This is an accepted version of a paper published in Knowledge and Information Systems. This paper has been peer-reviewed but does not include the final publisher proofcorrections or journal pagination.Citation for the published paper: verikas, a., guzaitis, j., gelzinis, a., bacauskiene, m. Abstract This paper presents a general framework for designing a fuzzy rule-based classifier. Structure and parameters of the classifier are evolved through a two-stage genetic search. To reduce the search space, the classifier structure is constrained by a tree created using the evolving SOM tree algorithm. Salient input variables are specific for each fuzzy rule and are found during the genetic search process. It is shown through computer simulations of four real world problems that a large number of rules and input variables can be eliminated from the model without deteriorating the classification accuracy. By contrast, the classification accuracy of unseen data is increased due to the elimination.
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.
Non-invasive physiological monitors are important subsystems of intensive care informatic systems. New innovative information methods and technology are presented for non-invasive human brain volumetric pulse wave physiological monitoring.Experimental study of a new, non-invasive ultrasonic intracranial pulse wave monitoring technology show the reactions of non-invasively recorded intracranial blood volume pulse waves (IB-VPW) on healthy volunteers in different human body positions. A group of 13 healthy volunteers was studied.Body posture caused IBVPW, subwaves changes, ΔP2 = 18% and ΔP3 = 11%. The value of the IBVPW amplitude's ratio in supine and upright positions was 1.55 ± 0.61.
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 ( = 54) and MN ( = 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 ( = 0.86) than in MN ( = 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.
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