In recent years the interest of biomedical and computer vision communities in acquisition and analysis of epidermal images increased because melanoma is one of the deadliest form of skin cancer and its early identification could save lives reducing unnecessary medical treatments. User-friendly automatic tools can be very useful for physicians and dermatologists in fact high-resolution images and their annotated data, combined with analysis pipelines and machine learning techniques, represent the base to develop intelligent and proactive diagnostic systems. In this work we present two skin lesion detection pipelines on dermoscopic medical images, by exploiting standard techniques combined with workarounds that improve results; moreover to highlight the performance we consider a set of metrics combined with pixel labeling and classification. A preliminary but functional evaluation phase has been conducted with a sub-set of hard-to-treat images, in order to check which proposed detection pipeline reaches the best results.

Pixel classification methods to detect skin lesions on dermoscopic medical images / Balducci, Fabrizio; Grana, Costantino. - 10485:(2017), pp. 444-455. (Intervento presentato al convegno 19th International Conference on Image Analysis and Processing, ICIAP 2017 tenutosi a Catania nel 11-15 September 2017) [10.1007/978-3-319-68548-9_41].

Pixel classification methods to detect skin lesions on dermoscopic medical images

BALDUCCI, FABRIZIO;GRANA, Costantino
2017

Abstract

In recent years the interest of biomedical and computer vision communities in acquisition and analysis of epidermal images increased because melanoma is one of the deadliest form of skin cancer and its early identification could save lives reducing unnecessary medical treatments. User-friendly automatic tools can be very useful for physicians and dermatologists in fact high-resolution images and their annotated data, combined with analysis pipelines and machine learning techniques, represent the base to develop intelligent and proactive diagnostic systems. In this work we present two skin lesion detection pipelines on dermoscopic medical images, by exploiting standard techniques combined with workarounds that improve results; moreover to highlight the performance we consider a set of metrics combined with pixel labeling and classification. A preliminary but functional evaluation phase has been conducted with a sub-set of hard-to-treat images, in order to check which proposed detection pipeline reaches the best results.
2017
19th International Conference on Image Analysis and Processing, ICIAP 2017
Catania
11-15 September 2017
10485
444
455
Balducci, Fabrizio; Grana, Costantino
Pixel classification methods to detect skin lesions on dermoscopic medical images / Balducci, Fabrizio; Grana, Costantino. - 10485:(2017), pp. 444-455. (Intervento presentato al convegno 19th International Conference on Image Analysis and Processing, ICIAP 2017 tenutosi a Catania nel 11-15 September 2017) [10.1007/978-3-319-68548-9_41].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1145952
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