{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T15:49:02Z","timestamp":1740152942654,"version":"3.37.3"},"reference-count":34,"publisher":"American Institute of Mathematical Sciences (AIMS)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MBE"],"published-print":{"date-parts":[[2022]]},"abstract":"<jats:p xml:lang=\"fr\">&lt;abstract&gt;\n\t\t\t&lt;p&gt;An important component of the computer systems of medical diagnostics in dermatology is the device for recognition of visual images (DRVI), which includes identification and segmentation procedures to build the image of the object for recognition. In this study, the peculiarities of the application of detection, classification and vector-difference approaches for the segmentation of textures of different types in images of dermatological diseases were considered. To increase the quality of segmented images in dermatologic diagnostic systems using a DRVI, an improved vector-difference method for spectral-statistical texture segmentation has been developed. The method is based on the estimation of the number of features and subsequent calculation of a specific texture feature, and it uses wavelets obtained by transforming the graph of the power function at the stage of contour segmentation. Based on the above, the authors developed a modulus for spectral-statistical texture segmentation, which they applied to segment images of psoriatic disease; the Pratt's criterion was used to assess the quality of segmentation. The reliability of the classification of the spectral-statistical texture images was confirmed by using the True Positive Rate (TPR) and False Positive Rate (FPR) metrics calculated on the basis of the confusion matrix. The results of the experimental research confirmed the advantage of the proposed vector-difference method for the segmentation of spectral-statistical textures. The method enables further supplementation of the vector of features at the stage of identification through the use of the most informative features based on characteristic points for different degrees and types of psoriatic disease.&lt;\/p&gt;\n\t\t&lt;\/abstract&gt;<\/jats:p>","DOI":"10.3934\/mbe.2022326","type":"journal-article","created":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T10:42:46Z","timestamp":1652092966000},"page":"6923-6939","source":"Crossref","is-referenced-by-count":2,"title":["Segmentation and identification of spectral and statistical textures for computer medical diagnostics in dermatology"],"prefix":"10.3934","volume":"19","author":[{"given":"Xinlin","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Viktor","family":"Krylov","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics and Information Technologies, Odessa National Polytechnic University, Odessa 65044, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Su","family":"Jun","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natalya","family":"Volkova","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics and Information Technologies, Odessa National Polytechnic University, Odessa 65044, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anatoliy","family":"Sachenko","sequence":"additional","affiliation":[{"name":"Department of Informatics and Teleinformatics, Kazimierz Pulaski University of Technology and Humanities in Radom, Radom 26600, Poland"},{"name":"Research Institute for Intelligent Computer Systems, West Ukrainian National University, Ternopil 46009, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Galina","family":"Shcherbakova","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Odessa National Polytechnic University, Odessa 65044, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jacek","family":"Woloszyn","sequence":"additional","affiliation":[{"name":"Department of Informatics and Teleinformatics, Kazimierz Pulaski University of Technology and Humanities in Radom, Radom 26600, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2321","reference":[{"key":"key-10.3934\/mbe.2022326-1","doi-asserted-by":"publisher","unstructured":"Y. Yemchenko, K. Ischeikin, Features of the clinical course of psoriasis in patients with obesity, <i>Med. Ecol. Probl.<\/i>, <b>24<\/b> (2020), 3-7. https:\/\/doi.org\/10.31718\/mep.2020.24.1-2.01","DOI":"10.31718\/mep.2020.24.1-2.01"},{"key":"key-10.3934\/mbe.2022326-2","doi-asserted-by":"publisher","unstructured":"K. G. Paulson, D. Gupta, T. S. Kim, J. R. Veatch, D. R. Byrd, S. Bhatia, et al., Age-specific incidence of melanoma in the United States, <i>JAMA Dermatol.<\/i>, <b>156<\/b> (2020), 57-64. https:\/\/doi.org\/10.1001\/jamadermatol.2019.3353.","DOI":"10.1001\/jamadermatol.2019.3353"},{"key":"key-10.3934\/mbe.2022326-3","doi-asserted-by":"publisher","unstructured":"M. J. Schaap, N. J. Cardozo, A. Patel, E. M. G. J. de Jong, B. van Ginneken, M. M. B. Seyger, Image-based automated psoriasis area severity index scoring by convolutional neural networks, <i>J. Eur. Acad. Dermatol. Venereol.<\/i>, <b>36<\/b> (2022), 68-75. https:\/\/doi.org\/10.1111\/jdv.17711","DOI":"10.1111\/jdv.17711"},{"key":"key-10.3934\/mbe.2022326-4","doi-asserted-by":"publisher","unstructured":"S. Gerdes, A. Krber, M. Biermann, C. Karnthaler, M. Reinhardt, Absolute and relative psoriasis area and severity index (PASI) treatment goals and their association with health-related quality of life, <i>J. Dermatol. Treat.<\/i>, <b>31<\/b> (2020), 470-475. https:\/\/doi.org\/10.1080\/09546634.2020.1746734","DOI":"10.1080\/09546634.2020.1746734"},{"key":"key-10.3934\/mbe.2022326-5","unstructured":"A. U. R. Butt, W. Ahmad, R. Ashraf, M. Asif, S. A Cheema, Computer aided diagnosis (CAD) for segmentation and classification of burnt human skin, in <i>Proceedings of the<\/i> <i>2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)<\/i>, (2019), 1-5. <a href=\"https:\/\/doi.org\/10.1109\/ICECCE47252.2019.8940758\" target=\"_blank\">https:\/\/doi.org\/10.1109\/ICECCE47252.2019.8940758<\/a>."},{"key":"key-10.3934\/mbe.2022326-6","doi-asserted-by":"publisher","unstructured":"J. Glaister, A. Wong, Segmentation of skin lesions from digital images using joint statistical texture distinctiveness, <i>IEEE Trans. Biomed. Eng.<\/i>, <b>61<\/b> (2014), 1220-1230. https:\/\/doi.org\/10.1109\/TBME.2013.2297622","DOI":"10.1109\/TBME.2013.2297622"},{"key":"key-10.3934\/mbe.2022326-7","doi-asserted-by":"crossref","unstructured":"F. S. Saleh, R. Azmi, Automatic multiple regions segmentation of dermoscopy images, in <i>Proceedings of the 2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)<\/i>, (2015), 24-29. <a href=\"https:\/\/doi.org\/10.1109\/AISP.2015.7123482\" target=\"_blank\">https:\/\/doi.org\/10.1109\/AISP.2015.7123482<\/a>","DOI":"10.1109\/AISP.2015.7123482"},{"key":"key-10.3934\/mbe.2022326-8","doi-asserted-by":"crossref","unstructured":"Y. Li, Z. Wu, S. Zhao, X. Wu, Y. Kuang, Y. Yan, et al., PSENet: Psoriasis severity evaluation network, in <i>Proceedings of the The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20)<\/i>, (2020), 800-807. <a href=\"https:\/\/doi.org\/10.1609\/aaai.v34i01.5424\" target=\"_blank\">https:\/\/doi.org\/10.1609\/aaai.v34i01.5424<\/a>","DOI":"10.1609\/aaai.v34i01.5424"},{"key":"key-10.3934\/mbe.2022326-9","doi-asserted-by":"publisher","unstructured":"E. F. L\u00f3pez, \u00c1. M. Garc\u00eda, L. S. D. Blanco, J. F. Marinero, J. P. Turiel, Automatic gauze tracking in laparoscopic surgery using image texture analysis, <i>Comput. Methods Programs Biomed.<\/i>, <b>190<\/b> (2020), 1-11. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105378","DOI":"10.1016\/j.cmpb.2020.105378"},{"key":"key-10.3934\/mbe.2022326-10","doi-asserted-by":"publisher","unstructured":"S. Wen, T. M. Kurc, Y. Gao, T. Zhao, J. H. Saltz, W. Zhu, Quality assessment in nucleus segmentation of histopathology image, <i>J. Pathol. Inf.<\/i>, <b>8<\/b> (2017), 1-12. https:\/\/doi.org\/10.4103\/jpi.jpi_43_17","DOI":"10.4103\/jpi.jpi_43_17"},{"key":"key-10.3934\/mbe.2022326-11","doi-asserted-by":"publisher","unstructured":"M. A. Kassem, K. M. Hosny, R. Dama\u0161evi\u010dius, M. M. Eltoukhy, Machine learning and deep learning methods for skin lesion classification and diagnosis: A systematic review, <i>Diagnostics<\/i>, <b>11<\/b> (2021), 1390. https:\/\/doi.org\/10.3390\/diagnostics11081390","DOI":"10.3390\/diagnostics11081390"},{"key":"key-10.3934\/mbe.2022326-12","doi-asserted-by":"publisher","unstructured":"L. Rundo, A. Tangherloni, P. Cazzaniga, M. Mistri, S. Galimberti, R. Woitek, et al., A CUDA-powered method for the feature extraction and unsupervised analysis of medical images, <i>J. Supercomput<\/i>., <b>77<\/b> (2021), 8514-8531. https:\/\/doi.org\/10.1007\/S11227-020-03565-8","DOI":"10.1007\/S11227-020-03565-8"},{"key":"key-10.3934\/mbe.2022326-13","doi-asserted-by":"publisher","unstructured":"V. K. Shrivastava, N. D. Londhe, R. S. Sonawane, J. S. Suri, Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: A first comparative study of its kind, <i>Comput. Methods Programs Biomed.<\/i>, <b>126<\/b> (2016), 98-109. https:\/\/doi.org\/10.1016\/j.cmpb.2015.11.013","DOI":"10.1016\/j.cmpb.2015.11.013"},{"key":"key-10.3934\/mbe.2022326-14","doi-asserted-by":"publisher","unstructured":"M. Hazgui, H. Ghazouani, W. Barhoumi, Genetic programming-based fusion of HOG and LBP features for fully automated texture classification, <i>Vis. Comput.<\/i>, <b>38<\/b> (2022), 457\u2013476. https:\/\/doi.org\/10.1007\/s00371-020-02028-8.","DOI":"10.1007\/s00371-020-02028-8"},{"key":"key-10.3934\/mbe.2022326-15","doi-asserted-by":"crossref","unstructured":"S. A. Almola, Find edge map for medical images based on texture characteristics, in <i>Proceedings of the<\/i> <i>2021 International Conference on Communication &#38; Information Technology (ICICT)<\/i>, (2021), 7-12. <a href=\"https:\/\/doi.org\/10.1109\/ICICT52195.2021.9568424\" target=\"_blank\">https:\/\/doi.org\/10.1109\/ICICT52195.2021.9568424<\/a>","DOI":"10.1109\/ICICT52195.2021.9568424"},{"key":"key-10.3934\/mbe.2022326-16","doi-asserted-by":"publisher","unstructured":"N. Sharma, A. K. Ray, S. Sharma, K. K. Shukla, S. Pradhan, L. M. Aggarwal, Segmentation and classification of medical images using texture-primitive features: Application of BAM-type artificial neural network, <i>J. Med. Phys.<\/i>, <b>33<\/b> (2008), 119\u2013126. https:\/\/doi.org\/10.4103\/0971-6203.42763","DOI":"10.4103\/0971-6203.42763"},{"key":"key-10.3934\/mbe.2022326-17","doi-asserted-by":"publisher","unstructured":"T. S. Jarad, A. J. Dawood, Computer-aided diagnosis psoriasis lesion using skin color and texture features, <i>Int. J. Comput. Digital Syst.<\/i><i>, <b>7<\/b><\/i> (2018), 145-154. https:\/\/doi.org\/10.12785\/IJCDS\/070303","DOI":"10.12785\/IJCDS\/070303"},{"key":"key-10.3934\/mbe.2022326-18","doi-asserted-by":"publisher","unstructured":"M. R. Mookiah, J. H. Tan, C. K. Chua, E. Y. Ng, A. Laude, L. Tong, Automated characterization and detection of diabetic retinopathy using texture measures, <i>J. Mechan. Med. Biol.<\/i><i>, <b>15<\/b><\/i> (2015), 1550045. https:\/\/doi.org\/10.1142\/S0219519415500451","DOI":"10.1142\/S0219519415500451"},{"key":"key-10.3934\/mbe.2022326-19","doi-asserted-by":"crossref","unstructured":"D. Gornea, D. Popescu, L. Ichim, Remote assessment of flooded areas based on inter-spectral statistical features, in <i>Proceedings of the 2016 39th International Conference on Telecommunications and Signal Processing (TSP)<\/i>, (2016), 707-710. <a href=\"https:\/\/doi.org\/10.1109\/TSP.2016.7760976\" target=\"_blank\">https:\/\/doi.org\/10.1109\/TSP.2016.7760976<\/a>","DOI":"10.1109\/TSP.2016.7760976"},{"key":"#cr-split#-key-10.3934\/mbe.2022326-20.1","doi-asserted-by":"crossref","unstructured":"D. N. Anh, Detection of lesion region in skin images by moment of patch, in <i>Proceedings of the 2016 IEEE RIVF International Conference on Computing &#38","DOI":"10.1109\/RIVF.2016.7800297"},{"key":"#cr-split#-key-10.3934\/mbe.2022326-20.2","unstructured":"Communication Technologies, Research, Innovation, and Vision for the Future (RIVF)<\/i>, (2016), 217-222. <a href=\"https:\/\/doi.org\/10.1109\/RIVF.2016.7800297\" target=\"_blank\">https:\/\/doi.org\/10.1109\/RIVF.2016.7800297<\/a>"},{"key":"key-10.3934\/mbe.2022326-21","doi-asserted-by":"publisher","unstructured":"V. N. Krylov, N. P. Volkova, Vector-difference texture segmentation method in technical and medical express diagnostic systems, <i>Herald Adv. Inf. Technol.<\/i>, <b>3<\/b> (2020), 174-186. https:\/\/doi.org\/10.15276\/hait.04.2020.2","DOI":"10.15276\/hait.04.2020.2"},{"key":"key-10.3934\/mbe.2022326-22","doi-asserted-by":"publisher","unstructured":"L. A. Soares, K. F. C\u00f4co, P. M. Ciarelli, E. O. T. Salles, A class-independent texture-separation method based on a pixel-wise binary classification, <i>Sensors<\/i>, <b>20<\/b> (2020), 5432. https:\/\/doi.org\/10.3390\/s20185432","DOI":"10.3390\/s20185432"},{"key":"key-10.3934\/mbe.2022326-23","unstructured":"T. Lacombe, H. Favreli\u00e8re, M. Pillet, Modal features for image texture classification, preprint, arXiv: 2005.01928. <a href=\"https:\/\/doi.org\/10.1016\/j.patrec.2020.04.036\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.patrec.2020.04.036<\/a>"},{"key":"key-10.3934\/mbe.2022326-24","doi-asserted-by":"publisher","unstructured":"D. Zahorodnia, Y. Pigovsky, P. Bykovyy, Canny-based method of image contour segmentation, <i>Int. J. Comput.<\/i>, <b>15<\/b> (2016), 200-205. https:\/\/doi.org\/10.47839\/ijc.15.3.853","DOI":"10.47839\/ijc.15.3.853"},{"key":"key-10.3934\/mbe.2022326-25","doi-asserted-by":"publisher","unstructured":"A. Zollanvari, B. Abibullaev, Bias correction for linear discriminant analysis, <i>Pattern Recognit. Lett.<\/i><i>,<\/i> <b>151<\/b> (2021), 41-47. https:\/\/doi.org\/10.1016\/J.PATREC.2021.07.026","DOI":"10.1016\/J.PATREC.2021.07.026"},{"key":"key-10.3934\/mbe.2022326-26","doi-asserted-by":"publisher","unstructured":"N. P. Volkova, V. N. Krylov, Hybrid texture identification method, <i>Herald Adv. Inf. Technol.<\/i>, <b>4<\/b> (2021), 123-134. https:\/\/doi.org\/10.15276\/hait.02.2021.2","DOI":"10.15276\/hait.02.2021.2"},{"key":"key-10.3934\/mbe.2022326-27","unstructured":"E. Li, Y. Huang, D. Xu, H. Li, Shape DNA: Basic generating functions for geometric moment invariants, preprint, arXiv: 1703.02242. <a href=\"https:\/\/arXiv.org\/pdf\/1703.02242.pdf\" target=\"_blank\">https:\/\/arXiv.org\/pdf\/1703.02242.pdf<\/a>"},{"key":"key-10.3934\/mbe.2022326-28","doi-asserted-by":"publisher","unstructured":"H. Nasser, P. Ngo, I. Debled-Rennesson, Dominant point detection based on discrete curve structure and applications, <i>J. Comput. Syst. Sci<\/i>., <b>95<\/b> (2018), 177-192. https:\/\/doi.org\/10.1016\/j.jcss.2017.07.007","DOI":"10.1016\/j.jcss.2017.07.007"},{"key":"key-10.3934\/mbe.2022326-29","unstructured":"R. Sjamaar, Lebesgue integration, <i>All Math You Missed<\/i>, (2021), 255-265. <a href=\"https:\/\/doi.org\/10.1017\/9781108992879.016\" target=\"_blank\">https:\/\/doi.org\/10.1017\/9781108992879.016<\/a>"},{"key":"key-10.3934\/mbe.2022326-30","doi-asserted-by":"crossref","unstructured":"G. Shcherbakova, S. Antoshchuk, A. Sachenko, M. Gerganov, M. Polyakova, V. Krylov, Areal multistart method of optimization for image recognition, in <i>Proceedings of the IEEE Second International Conference on Data Stream Mining &#38; Processing<\/i>, (2018), 605-608. <a href=\"https:\/\/doi.org\/10.1109\/DSMP.2018.8478551\" target=\"_blank\">https:\/\/doi.org\/10.1109\/DSMP.2018.8478551<\/a>","DOI":"10.1109\/DSMP.2018.8478551"},{"key":"key-10.3934\/mbe.2022326-31","doi-asserted-by":"crossref","unstructured":"G. Shcherbakova, V. Krylov, B. Rusyn, A. Sachenko, P. Bykovyy, D. Zahorodnia, et al., Optimization methods on the wavelet transformation base for technical diagnostic information systems, in <i>Proceedings of the 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS'2021)<\/i>, (2021), 767-773. <a href=\"https:\/\/doi.org\/10.1109\/IDAACS53288.2021.9660927\" target=\"_blank\">https:\/\/doi.org\/10.1109\/IDAACS53288.2021.9660927<\/a>","DOI":"10.1109\/IDAACS53288.2021.9660927"},{"key":"key-10.3934\/mbe.2022326-32","doi-asserted-by":"publisher","unstructured":"H. Zhengbing, I. Tereikovskyi, D. Chernyshev, L. Tereikovska, O. Tereikovskyi, W. Dong, Procedure for processing biometric parameters based on wavelet transformations, <i>Int. J. Mod. Educ. Comput. Sci<\/i>, <b>13<\/b> (2021), 11-22. https:\/\/doi.org\/10.5815\/ijmecs.2021.02.02","DOI":"10.5815\/ijmecs.2021.02.02"},{"key":"key-10.3934\/mbe.2022326-33","doi-asserted-by":"crossref","unstructured":"I. Paliy, A. Sachenko, V. Koval, Y. Kurylyak, Approach to face recognition using neural networks, in <i>Proceedings of the IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS'2005)<\/i>, (2005), 112-115. doi: <a href=\"http:\/\/dx.doi.org\/10.1109\/IDAACS.2005.282951\" target=\"_blank\">10.1109\/IDAACS.2005.282951<\/a>","DOI":"10.1109\/IDAACS.2005.282951"}],"container-title":["Mathematical Biosciences and Engineering"],"original-title":[],"link":[{"URL":"http:\/\/www.aimspress.com\/article\/doi\/10.3934\/mbe.2022326?viewType=html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T01:16:49Z","timestamp":1652923009000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.aimspress.com\/article\/doi\/10.3934\/mbe.2022326"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":34,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022]]}},"URL":"https:\/\/doi.org\/10.3934\/mbe.2022326","relation":{},"ISSN":["1551-0018"],"issn-type":[{"type":"print","value":"1551-0018"}],"subject":[],"published":{"date-parts":[[2022]]}}}