IEEE J Biomed Health Inform - Laryngeal Tumor Detection and Classification in Endoscopic Video.

Tópicos

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Resumo

The development of the narrow-band imaging has been increasing the interest of medical specialists in the study of laryngeal microvascular network to establish diagnosis without biopsy and pathological examination. A possible solution to this challenging problem is presented in this paper, which proposes an automatic method based on anisotropic filtering and matched filter to extract the lesion area and segment blood vessels. Lesion classification is then performed based on a statistical analysis of the blood vessels' characteristics, such as thickness, tortuosity and density. Here, the presented algorithm is applied to 50 NBI endoscopic images of laryngeal diseases and the segmentation and classification accuracies are investigated. The experimental results show the proposed algorithm provides reliable results, reaching an overall classification accuracy rating of 84.3%. This is a highly motivating preliminary result that proves the feasibility of the new method and supports the investment in further research and development to translate this work into clinical practice. Furthermore, to our best knowledge this is the first time image processing is used to automatically classify laryngeal tumors in endoscopic videos based on tumor vascularization characteristics. Therefore, the introduced system represents an innovation in biomedical and health informatics.

Resumo Limpo

develop narrowband imag increas interest medic specialist studi laryng microvascular network establish diagnosi without biopsi patholog examin possibl solut challeng problem present paper propos automat method base anisotrop filter match filter extract lesion area segment blood vessel lesion classif perform base statist analysi blood vessel characterist thick tortuos densiti present algorithm appli nbi endoscop imag laryng diseas segment classif accuraci investig experiment result show propos algorithm provid reliabl result reach overal classif accuraci rate high motiv preliminari result prove feasibl new method support invest research develop translat work clinic practic furthermor best knowledg first time imag process use automat classifi laryng tumor endoscop video base tumor vascular characterist therefor introduc system repres innov biomed health informat

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