Computer vision and digital imaging technology in melanoma detection

Voigt, H.; Classen, Richarda

doi:10.1053/sonc.2002.34109

Cited by 27 publications

(19 citation statements)

References 155 publications

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“…MRI scores were determined for each of these ROIs using the standardization equation (Eq. [1]). In addition, MRI scores were determined for the clusters from which the ROIs were selected (see Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The range r 2 Ϫ r 1 determines the resolution desired for tissue differentiation. Equation [1] assigns signature (score) r 1 to the normal brain matter and signature (score) r 2 to CSF. It assigns a score (signature number) to each of the lesion zones based on its level of similarities (in terms of multiparametric MRI) to the normal brain matter and CSF.…”

Section: Proposed Methods For Standardization Of Segmentation Resultsmentioning

confidence: 99%

“…THE ULTIMATE GOAL of medical imaging and analysis is to generate and extract important clinical information that would improve diagnosis and treatment of disease (1)(2)(3)(4)(5)(6)(7)(8). In particular, the accurate identification and discrimination of viable from necrotic tissue after stroke would improve the management of patients.…”

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confidence: 99%

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MRI tissue characterization of experimental cerebral ischemia in rat

Soltanian-Zadeh

Pasnoor

Hammoud

et al. 2003

Magnetic Resonance Imaging

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Purpose:To extend the ISODATA image segmentation method to characterize tissue damage in stroke, by generating an MRI score for each tissue that corresponds to its histological damage. Materials and Methods:After preprocessing and segmentation (using ISODATA clustering), the proposed method scores tissue regions between 1 and 100. Score 1 is assigned to normal brain matter (white or gray matter), and score 100 to cerebrospinal fluid (CSF). Lesion zones are assigned a score based on their relative levels of similarities to normal brain matter and CSF. To evaluate the method, 15 rats were imaged by a 7T MRI system at one of three time points (acute, subacute, chronic) after MCA occlusion. Then they were killed and their brains were sliced and prepared for histological studies. MRI of two or three slices of each rat brain (using two DWI (b ϭ 400, b ϭ 800), one PDWI, one T2WI, and one T1WI) was performed, and an MRI score between 1 and 100 was determined for each region. Segmented regions were mapped onto the histology images and scored on a scale of 1-10 by an experienced pathologist. The MRI scores were validated by comparison with histology scores. To this end, correlation coefficients between the two scores (MRI and histology) were determined. Results:Experimental results showed excellent correlations between MRI and histology scores at different time points. Depending on the reference tissue (gray matter or white matter) used in the standardization, the correlation coefficients ranged from 0.73 (P Ͻ 0.0001) to 0.78 (P Ͻ 0.0001) using the entire dataset, including acute, subacute, and chronic time points. This suggests that the proposed multiparametric approach accurately identified and characterized ischemic tissue in a rat model of cerebral ischemia at different stages of stroke evolution. Conclusion:The proposed approach scores tissue regions and characterizes them using unsupervised clustering and multiparametric image analysis techniques. The method can be used for a variety of applications in the field of computer-aided diagnosis and treatment, including evaluation of response to treatment. For example, volume changes for different zones of the lesion over time (e.g., tissue recovery) can be evaluated.

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Section: Resultsmentioning

confidence: 99%

Section: Proposed Methods For Standardization Of Segmentation Resultsmentioning

confidence: 99%

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MRI tissue characterization of experimental cerebral ischemia in rat

Soltanian-Zadeh

Pasnoor

Hammoud

et al. 2003

Magnetic Resonance Imaging

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“…Early melanoma detection has gained major attention for the development of non-invasive imaging techniques [1] [2] and computer-based solution [3] [4]. Those efforts try to bring objective measurement of skin lesions.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of hyperspectral cutaneous data from an artificial neural network-based multispectral imaging system

Jolivot

Vabres

Marzani

2011

Computerized Medical Imaging and Graphics

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The development of an integrated MultiSpectral Imaging (MSI) system yielding hyperspectral cubes by means of artificial neural networks is described. The MSI system is based on a CCD camera, a rotating wheel bearing a set of seven interference filters, a light source and a computer. The resulting device has been elaborated for in vivo imaging of skin lesions. It provides multispectral images and is coupled with a software reconstructing hyperspectral cubes from multispectral images. Reconstruction is performed by a neural network-based algorithm using hetero-associative memories. The resulting hyperspectral cube provides skin optical reflectance spectral data combined with bidimensional spatial information. This combined information will hopefully improve diagnosis and follow-up in a range of skin disorders from skin cancer to inflammatory diseases.

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“…Color is an important attribute for primary skin efflorescences [3]. Color features have proven beneficial in many applications and medical sciences, especially for the recognition of skin lesions [14,40,43,38,34,19,37,25,46,18] or the classification of skin cancer [28,44,1,16,10,42]. A dermatologist might be interested in pictures of similar skin lesions in comparison to an actual case to verify the diagnosis or confer with similar symptoms.…”

Section: Introductionmentioning

confidence: 99%