An efficient multi-level pre-processing algorithm for the enhancement of dermoscopy images in melanoma detection

In this paper, a multi-level algorithm for pre-processing of dermoscopy images is proposed, which helps in improving the quality of the raw images, making it suitable for skin lesion detection. This multi-level pre-processing method has a positive impact on automated skin lesion segmentation using Regularized Extreme Learning Machine. Raw images are subjected to de-noising, illumination correction, contrast enhancement, sharpening, reflection removal, and virtual shaving before the skin lesion segmentation. The Non-Local Means (NLM) filter with lowest Blind Reference less Image Spatial Quality Evaluator (BRISQUE) score exhibits better de-noising of dermoscopy images. To suppress uneven illumination, gamma correction is subjected to the denoised image. The Robust Image Contrast Enhancement (RICE) algorithm is used for contrast enhancement, and produces enhanced images with better structural preservation and negligible loss of information. Unsharp masking for sharpening exhibits low BRISQUE scores for better sharpening of fine details in an image. Output images produced by the phase congruency-based method in virtual shaving show high similarity with ground truth images as the hair is removed completely from the input images. Obtained scores at each stage of pre-processing framework show that the performance is superior compared to all the existing methods, both qualitatively and quantitatively, in terms of uniform contrast, preservation of information content, removal of undesired information, and elimination of artifacts in melanoma images. The output of the proposed system is assessed qualitatively and quantitatively with and without pre-processing of dermoscopy images. From the overall evaluation results, it is found that the segmentation of skin lesion is more efficient using Regularized Extreme Learning Machine if the multi-level pre-processing steps are used in proper sequence.

Hybrid enhancement algorithm for nailfold images with large fields of view

Collecting and analyzing human nailfold images is an important component of studying human microcirculation. However, the large-field-of-view and high-resolution nailfold images captured by research microscopes introduce issues such as uneven brightness, low imaging contrast, and unclear vascular contours. To overcome these issues, this paper proposes a hybrid enhancement algorithm for nailfold images with large fields of view. First, adaptive histogram equalization with limited contrast (Clahe) is used to redistribute gray levels to enhance the brightness and contrast of images. Next, nonlocal means denoising (NL-means) is used to remove the noise amplified by Clahe algorithm. Finally, unsharp masking (Usm) is used to enhance the edge contour information of nailfold blood vessels. Comparing the enhanced images reveals that the hybrid enhancement algorithm improves the brightness and contrast of the nailfold image, makes the nailfold vessel contour more obvious, and the image noise continues to remain small, and it obtains the best visual effect. It is superior to other algorithms in terms of objective indicators and subjective evaluation.

Tri-Scan: A Three Stage Color Enhancement Tool for Endoscopic Images

Modern endoscopes play a significant role in diagnosing various gastrointestinal (GI) tract related diseases where the visual quality of endoscopic images helps improving the diagnosis. This article presents an image enhancement method for color endoscopic images that consists of three stages, and hence termed as "Tri-scan" enhancement: (1) tissue and surface enhancement: a modified linear unsharp masking is used to sharpen the surface and edges of tissue and vascular characteristics; (2) mucosa layer enhancement: an adaptive sigmoid function is employed on the R plane of the image to highlight micro-vessels of the superficial layers of the mucosa and submucosa; and (3) color tone enhancement: the pixels are uniformly distributed to create an enhanced color effect to highlight the subtle micro-vessels, mucosa and tissue characteristics. The proposed method is used on a large data set of low contrast color white light images (WLI). The results are compared with three existing enhancement techniques: Narrow Band Imaging (NBI), Fuji Intelligent Color Enhancement (FICE) and i-scan Technology. The focus value and color enhancement factor show that the enhancement level achieved in the processed images is higher compared to NBI, FICE and i-scan images.