Melanoma Recognition with an Ensemble of Techniques for Segmentation and a Structural Analysis for Classification

Rasche, Christoph

doi:10.48550/arxiv.1807.06905

Cited by 1 publication

(1 citation statement)

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“…Segmentation and contour extraction are important steps towards the analysis of digital images in the medical field, where such images are routinely used in a multitude of different applications [9]. Segmentation algorithms, based on structural analysis, continue to be used, often as an ensemble of segmentation techniques, especially in critical applications, such as lesion localization [10,11]. Other approaches, based on biased normalized cuts or light techniques, are also devised [12,13].…”

Section: Introductionmentioning

confidence: 99%

A Smartphone-Based Cell Segmentation to Support Nasal Cytology

et al. 2020

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Rhinology studies the anatomy, physiology, and diseases affecting the nasal region—one of the most modern techniques to diagnose these diseases is nasal cytology, which involves microscopic analysis of the cells contained in the nasal mucosa. The standard clinical protocol regulates the compilation of the rhino-cytogram by observing, for each slide, at least 50 fields under an optical microscope to evaluate the cell population and search for cells important for diagnosis. The time and effort required for the specialist to analyze a slide are significant. In this paper, we present a smartphones-based system to support cell segmentation on images acquired directly from the microscope. Then, the specialist can analyze the cells and the other elements extracted directly or, alternatively, he can send them to Rhino-cyt, a server system recently presented in the literature, that also performs the automatic cell classification, giving back the final rhinocytogram. This way he significantly reduces the time for diagnosing. The system crops cells with sensitivity = 0.96, which is satisfactory because it shows that cells are not overlooked as false negatives are few, and therefore largely sufficient to support the specialist effectively. The use of traditional image processing techniques to preprocess the images also makes the process sustainable from the computational point of view for medium–low end architectures and is battery-efficient on a mobile phone.

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