The Utility of Digital Clinical Photographs in Dermatopathology

Fogelberg, Anneli C.; Ioffreda, Michael D.; Helm, Klaus F.

doi:10.1007/s10227-004-0104-x

Cited by 15 publications

(14 citation statements)

References 15 publications

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“…With respect to dermato-histopathology [53], what was said for pathology is applicable. Since there is hardly any SPIS solely dedicated to dermato-histopathology, an inclusion of the MMOs into the MD-PACS using Type-II is the most feasible approach.…”

Section: Dermatologymentioning

confidence: 97%

Enterprise imaging and multi-departmental PACS

Bergh

2006

Eur Radiol

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The aim of this review is to present the status of digital image acquisition and archiving outside of radiology and to describe the technical concepts and possibilities of how a "radiology" Picture Archiving and Communication System (PACS) can become a multi-departmental (MD-)PACS. First the principles of system integration technology are explained and illustrated by the description of a typical radiology system integration. Then four types of modality integration approaches are defined: the direct modality integration (Type-I), the integration via DICOM acquisition software (Type-II) the integration via specialised systems either with (Type-III) or without PACS connection (Type-IV). The last section is dedicated to the presentation of the PACS requirements of selected interdisciplinary modality types [Endoscopy, Ultrasound and Electrocardiography (ECG)] and clinical disciplines (Pathology, Dermatology, Ophthalmology and Cardiology), which are then compared with the technical possibilities of a MD-PACS.

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Section: Dermatologymentioning

confidence: 97%

Enterprise imaging and multi-departmental PACS

Bergh

2006

Eur Radiol

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“…A diagnosis is roughly reached after routine physical exams including observation and physical examination of skin lesions. The second step is skin biopsy image analysis [8]. It is accepted that the second step is a complement of the first one [9] [10], in case that doctor has less confidence or even can't make decision only by inspecting skin surface.…”

Section: Introductionmentioning

confidence: 99%

Multi-instance learning for skin biopsy image features recognition

Zhang

Shu

Liang

et al. 2012

2012 IEEE International Conference on Bioinformatics and Biomedicine

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In this paper, a multi-instance learning framework is introduced to solve the problem of skin biopsy image features recognition. Previously reported methods for skin surface images were mostly based on color features extraction. They are incapable to be directly applied to skin biopsy image features recognition because biopsy images are often dyed and have obvious inner structures with different textures. Therefore, we regard skin biopsy images as multi-instance samples, whose instances are regions or structures captured by applying Normalized Cut. Texture feature extraction methods are used to express each region as a vectorial expression. Then two multi-instance learning algorithms reported successful in various image retrieval tasks were applied. Nine features were manually selected as target features to evaluate the proposed method on a skin disease diagnosis datasets of 6579 biopsy images from 2010 to 2011. The result showed that the proposed method is effective and medically acceptable.

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“…Skin biopsy images reflect the pathological changes behind skin lesions at a microscopic level. It is well accepted that histopathology is the gold standard of diagnosing a skin disease [2]. Secondly, to reach a correct diagnosis, a doctor needs not only professional knowledge and rich experience in inspecting skin lesion, but also the field of skin histopathology.…”

Section: Introductionmentioning

confidence: 99%

A sparse Bayesian multi-instance multi-label model for skin biopsy image analysis

Zhang

Huang

et al. 2013

2013 IEEE International Conference on Bioinformatics and Biomedicine

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As a significant complement for skin surface images, skin biopsy image may reveal causes and severity of many skin diseases, especially in the case of skin cancer inspection. With rapid increment of skin disease patients, computational methods have been introduced for automatic classification of skin images. However, due to the complex relationship among annotation terms and features of local regions, it becomes a great challenge for skin biopsy image feature recognition and annotation. In this paper, we attempt to model the potential knowledge and experience of doctors on skin biopsy image annotation by using a recent proposed machine learning model, named multi-instance multi-label (MIML) model. We show that the relationship among annotation terms and skin biopsy images is naturally consistent with the MIML framework. We further propose a sparse Bayesian MIML algorithm which can produce a probability indicating the confidence of annotating a term. The proposed algorithm framework is evaluated on a real dataset from a large local hospital containing 12,700 skin biopsy images. The results show that the proposed algorithm is effective and prominent.

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The Utility of Digital Clinical Photographs in Dermatopathology

Abstract: In certain cases the inclusion of a digital image may help a dermatopathologist. Digital imaging was found to be most helpful in evaluation of inflammatory disorders.

Cited by 15 publications

References 15 publications

Enterprise imaging and multi-departmental PACS

Enterprise imaging and multi-departmental PACS

Multi-instance learning for skin biopsy image features recognition

A sparse Bayesian multi-instance multi-label model for skin biopsy image analysis

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