Skin lesion classification using relative color features

Cheng, Yue; Swamisai, Ragavendar; Umbaugh, Scott E.; Moss, Randy Hays; Stoecker, William V.; Teegala, Saritha; Srinivasan, Subhashini K.

doi:10.1111/j.1600-0846.2007.00261.x

Cited by 92 publications

(49 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[21][22][23][24][25] To our best knowledge, no attempt was done for extraction of blood oxygenation using PCA from RGB or multispectral images or for comparison of PCA to reconstruction results. The reconstruction results of four healthy volunteers undergoing occlusion of the upper arm and imaged at the forearm ͑Fig.…”

Section: Discussionmentioning

confidence: 99%

“…21 Applied to RGB images, PCA was used on relative color features for unsupervised lesion classification. [22][23][24] When imaging the skin in the visible light range, the dominant absorbing materials are blood and melanin and should therefore explain most of the variance in multispectral data. Previous work by Tsumura et al 25 showed that skin color in digital RGB images can be described by attributing melanin and blood to the first two principal components.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Principal component model of multispectral data for near real-time skin chromophore mapping

et al. 2010

View full text Add to dashboard Cite

Abstract. Multispectral images of skin contain information on the spatial distribution of biological chromophores, such as blood and melanin. From this, parameters such as blood volume and blood oxygenation can be retrieved using reconstruction algorithms. Most such approaches use some form of pixelwise or volumetric reconstruction code. We explore the use of principal component analysis ͑PCA͒ of multispectral images to access blood volume and blood oxygenation in near real time. We present data from healthy volunteers under arterial occlusion of the forearm, experiencing ischemia and reactive hyperemia. Using a two-layered analytical skin model, we show reconstruction results of blood volume and oxygenation and compare it to the results obtained from our new spectral analysis based on PCA. We demonstrate that PCA applied to multispectral images gives near equivalent results for skin chromophore mapping and quantification with the advantage of being three orders of magnitude faster than the reconstruction algorithm.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Principal component model of multispectral data for near real-time skin chromophore mapping

et al. 2010

View full text Add to dashboard Cite

show abstract

“…A lot of efforts have been dedicated in solving this challenging problem. Early investigations are achieved to apply low-level hand-crafted features to distinguish melanomas from non-melanoma skin lesions, including, color [5],shape [6] and texture [7], [8]. Some of the researchers are undertaken to employ feature selection algorithms to select proper features and utilized combinations of these low-level features to improve the recognition performance [9], [10].…”

Section: Literature Surveymentioning

confidence: 99%

Design and Implementation of CNN for detecting Melanoma through Image Processing

Ranjan¹

2018

IJRASET

View full text Add to dashboard Cite

Melanoma is a type of skin cancer which is caused by prolonged exposure to u-v light. Early days melanoma detected through dermoscopy where doctor tries to recognize melanoma based on his experience and assumption. But many cases patient who suffers from melanoma dies because of it not detected early stages. In recent years, several procedures have been proposed to detect and analyze skin cancer. Early stages only skin cancer can be classified as malignant or benign. All proposed methods contribute better values of precision, sensitivity, specificity, and accuracy.

show abstract

“…This improved spectral resolution allows for better tissue discrimination, however this is hampered by the presence of large illumination changed across the image. As lesion classification methods [3] rely on many features that can be observed on a lesion, such as color and fine structures, they are quite sensitive to illumination changes. Our motivation is to exploit the additional information of multispectral imaging, in order to compensate for such illumination changes, while preserving both morphological and colorimetric (spectral) features of the lesions.…”

Section: Introductionmentioning

confidence: 99%

Illumination Compensation and Normalization Using Low-Rank Decomposition of Multispectral Images in Dermatology

Duliu

Brosig

Ognawala

et al. 2015

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. When attempting to recover the surface color from an image, modelling the illumination contribution per-pixel is essential. In this work we present a novel approach for illumination compensation using multispectral image data. This is done by means of a low-rank decomposition of representative spectral bands with prior knowledge of the reflectance spectra of the imaged surface. Experimental results on synthetic data, as well as on images of real lesions acquired at the university clinic, show that the proposed method significantly improves the contrast between the lesion and the background.

show abstract

Skin lesion classification using relative color features

Cited by 92 publications

References 16 publications

Principal component model of multispectral data for near real-time skin chromophore mapping

Principal component model of multispectral data for near real-time skin chromophore mapping

Design and Implementation of CNN for detecting Melanoma through Image Processing

Illumination Compensation and Normalization Using Low-Rank Decomposition of Multispectral Images in Dermatology

Contact Info

Product

Resources

About