Definition of an automated Content-Based Image Retrieval (CBIR) system for the comparison of dermoscopic images of pigmented skin lesions

Department of Biochemistry, Section of Pathology, Second University of Naples, Naples, Italy.
BioMedical Engineering OnLine (Impact Factor: 1.43). 09/2009; 8(1):18. DOI: 10.1186/1475-925X-8-18
Source: PubMed

ABSTRACT New generations of image-based diagnostic machines are based on digital technologies for data acquisition; consequently, the diffusion of digital archiving systems for diagnostic exams preservation and cataloguing is rapidly increasing. To overcome the limits of current state of art text-based access methods, we have developed a novel content-based search engine for dermoscopic images to support clinical decision making.
To this end, we have enrolled, from 2004 to 2008, 3415 caucasian patients and collected 24804 dermoscopic images corresponding to 20491 pigmented lesions with known pathology. The images were acquired with a well defined dermoscopy system and stored to disk in 24-bit per pixel TIFF format using interactive software developed in C++, in order to create a digital archive.
The analysis system of the images consists in the extraction of the low-level representative features which permits the retrieval of similar images in terms of colour and texture from the archive, by using a hierarchical multi-scale computation of the Bhattacharyya distance of all the database images representation with respect to the representation of user submitted (query).
The system is able to locate, retrieve and display dermoscopic images similar in appearance to one that is given as a query, using a set of primitive features not related to any specific diagnostic method able to visually characterize the image. Similar search engine could find possible usage in all sectors of diagnostic imaging, or digital signals, which could be supported by the information available in medical archives.

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Available from: Alfonso Baldi, Sep 26, 2015
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    • "Following an approach that should be practical and intuitive to dermatologists, the images considered in this study are acquired by means of a consumer-grade digital camera with a dermatoscope attached. This simple image acquisition setup has been previously discussed, for instance in Gewirtzman and Braun [22], and has been used in the visual comparison system of Baldi et al. [23]. "
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    ABSTRACT: Background It is often difficult to differentiate early melanomas from benign melanocytic nevi even by expert dermatologists, and the task is even more challenging for primary care physicians untrained in dermatology and dermoscopy. A computer system can provide an objective and quantitative evaluation of skin lesions, reducing subjectivity in the diagnosis. Objective Our objective is to make a low-cost computer aided diagnostic tool applicable in primary care based on a consumer grade camera with attached dermatoscope, and compare its performance to that of experienced dermatologists. Methods and Material We propose several new image-derived features computed from automatically segmented dermoscopic pictures. These are related to the asymmetry, color, border, geometry, and texture of skin lesions. The diagnostic accuracy of the system is compared with that of three dermatologists. Results With a data set of 206 skin lesions, 169 benign and 37 melanomas, the classifier was able to provide competitive sensitivity (86%) and specificity (52%) scores compared with the sensitivity (85%) and specificity (48%) of the most accurate dermatologist using only dermoscopic images. Conclusion We show that simple statistical classifiers can be trained to provide a recommendation on whether a pigmented skin lesion requires biopsy to exclude skin cancer with a performance that is comparable to and exceeds that of experienced dermatologists.
    Artificial Intelligence in Medicine 12/2013; 60(1). DOI:10.1016/j.artmed.2013.11.006 · 2.02 Impact Factor
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    • "In the near future, it should be possible to use PACS to search for and retrieve lesion images that are similar in appearance to previously queried images, based on a set of primitive image characteristics which are not related to any specific diagnostic method able to visually characterize the image, by utilizing reliable and practical methods developed for quantifying the similarity of a pair of images for further visual comparison by radiologists (39-42). Similar content-based image search engines will no doubt be of interest to all medical specialties that make use of medical imaging or digital biomedical signals, drawing support from the information available in medical archives (40). "
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    ABSTRACT: Within six months of the discovery of X-ray in 1895, the technology was used to scan the interior of the human body, paving the way for many innovations in the field of medicine, including an ultrasound device in 1950, a CT scanner in 1972, and MRI in 1980. More recent decades have witnessed developments such as digital imaging using a picture archiving and communication system, computer-aided detection/diagnosis, organ-specific workstations, and molecular, functional, and quantitative imaging. One of the latest technical breakthrough in the field of radiology has been imaging genomics and robotic interventions for biopsy and theragnosis. This review provides an engineering perspective on these developments and several other megatrends in radiology.
    Korean journal of radiology: official journal of the Korean Radiological Society 02/2013; 14(2):139-153. DOI:10.3348/kjr.2013.14.2.139 · 1.57 Impact Factor
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    • "The approach of our CBIR system for dermoscopic images, that is FIDE [39], is to document the image analysis side of the diagnostic process, focusing on accompanying and aiding it and on providing efficient digital atlas navigation aiming both at providing precision (cases most similar to the one under analysis) and clarifying context (similar cases in different categories). Instead of getting indications on a possible diagnosis by an automated interpretation system, the doctor needs to be recognized in her/his role and aided by an efficient search system able to present a number of similar cases from a large atlas. "
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    ABSTRACT: Dermoscopy (dermatoscopy, epiluminescence microscopy) is a non-invasive diagnostic technique for the in vivo observation of pigmented skin lesions (PSLs), allowing a better visualization of surface and subsurface structures (from the epidermis to the papillary dermis). This diagnostic tool permits the recognition of morphologic structures not visible by the naked eye, thus opening a new dimension in the analysis of the clinical morphologic features of PSLs. In order to reduce the learning-curve of non-expert clinicians and to mitigate problems inherent in the reliability and reproducibility of the diagnostic criteria used in pattern analysis, several indicative methods based on diagnostic algorithms have been introduced in the last few years. Recently, numerous systems designed to provide computer-aided analysis of digital images obtained by dermoscopy have been reported in the literature. The goal of this article is to review these systems, focusing on the most recent approaches based on content-based image retrieval systems (CBIR).
    Cancers 06/2010; 2(2):262-273. DOI:10.3390/cancers2020262
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