Melanoma Detection in Dermoscopic Images using Color Features

Dermoscopy and skin imaging light sources: a comparison and review of spectral power distribution and color consistency

SIGNIFICANCE

Dermoscopes incorporate light, polarizers, and optical magnification into a handheld tool that is commonly used by dermatologists to evaluate skin findings. Diagnostic accuracy is improved when dermoscopes are used, and some major artificial intelligence (AI) projects have been accomplished using dermocopic images. Color rendering consistency and fidelity are crucial for clinical diagnostics, AI, and image processing applications.

AIM

With many devices available on the market, our objective was to measure the emission spectra of various dermoscopes, compare them with other light sources, and illustrate variations in reflected colors from images of a reference sample.

APPROACH

A spectrometer measured the spectral power distribution (SPD) produced by four dermoscope models and three alternate light sources, illustrating differences in the emission spectra. Most dermoscopes use light-emitting diodes (LEDs), which are inconsistent when compared with one another. An LED was compared with halogen, xenon-arc, and daylight sources. Images of a micro ColorChecker were acquired from several sources, and three specific colors were selected to compare in CIELAB color space. Color consistency and color fidelity measured by color rendering index (CRI) and TM-30-18 graphical vectors show variation in saturation and chroma fidelity.

RESULTS

A marked degree of variation was observed in both the emission and reflected light coming from different dermoscopes and compared with other sources. The same chromophores appeared differently depending on the light source used.

CONCLUSIONS

A lack of uniform illumination resulted in inconsistent image color and likely impacted metamerism and visibility of skin chromophores in real-world settings. Artificial light in skin examinations, especially LEDs, may present challenges for the visual separation of specific colors. Attention to LEDs SPD may be important, especially as the field increases dependency on machine/computer vision.

Automatic segmentation and melanoma detection based on color and texture features in dermoscopic images

PURPOSE

Melanoma is known as the most aggressive form of skin cancer and one of the fastest growing malignant tumors worldwide. Several computer-aided diagnosis systems for melanoma have been proposed, still, the algorithms encounter difficulties in the early stage of lesions. This paper aims to discriminate melanoma and benign skin lesion in dermoscopic images.

METHODS

The proposed algorithm is based on the color and texture of skin lesions by introducing a novel feature extraction technique. The algorithm uses an automatic segmentation based on k-means generating a fairly accurate mask for each lesion. The feature extraction consists of the existing and novel color and texture attributes measuring how color and texture vary inside the lesion. To find the optimal results, all the attributes are extracted from lesions in five different color spaces (RGB, HSV, Lab, XYZ, and YCbCr) and used as the inputs for three classifiers (K nearest neighbors, support vector machine , and artificial neural network).

RESULTS

The PH2 set is used to assess the performance of the proposed algorithm. The results of our algorithm are compared to the results of published articles that used the same dataset, and it shows that the proposed method outperforms the state of the art by attaining a sensitivity of 99.25%, specificity of 99.58%, and accuracy of 99.51%.

CONCLUSION

The final results show that the colors combined with texture are powerful and relevant attributes for melanoma detection and show improvement over the state of the art.

Microscopic melanoma detection and classification: A framework of pixel-based fusion and multilevel features reduction

The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel-based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean-based function is implemented and fed input to top-hat and bottom-hat filters which later fused for contrast stretching, (b) seed region growing and graph-cut method-based lesion segmentation and fused both segmented lesions through pixel-based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy-based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.