Background And Objective: During the initial stages, skin lesions may not have sufficient intensity difference or contrast from the background region on dermatological macro-images. The lack of proper light exposure at the time of capturing the image also reduces the contrast. Low contrast between lesion and background regions adversely impacts segmentation. Enhancement techniques for improving the contrast between lesion and background skin on dermatological macro-images are limited in the literature. An EfficientNet-based modified sigmoid transform for enhancing the contrast on dermatological macro-images is proposed to address this issue.
Methods: A modified sigmoid transform is applied in the HSV color space. The crossover point in the modified sigmoid transform that divides the macro-image into lesion and background is predicted using a modified EfficientNet regressor to exclude manual intervention and subjectivity. The Modified EfficientNet regressor is constructed by replacing the classifier layer in the conventional EfficientNet with a regression layer. Transfer learning is employed to reduce the training time and size of the dataset required to train the modified EfficientNet regressor. For training the modified EfficientNet regressor, a set of value components extracted from the HSV color space representation of the macro-images in the training dataset is fed as input. The corresponding set of ideal crossover points at which the values of Dice similarity coefficient (DSC) between the ground-truth images and the segmented output images obtained from Otsu's thresholding are maximum, is defined as the target.
Results: On images enhanced with the proposed framework, the DSC of segmented results obtained by Otsu's thresholding increased from 0.68 ± 0.34 to 0.81 ± 0.17.
Conclusions: The proposed algorithm could consistently improve the contrast between lesion and background on a comprehensive set of test images, justifying its applications in automated analysis of dermatological macro-images.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.cmpb.2022.106935 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A Novel Hair Restoration Technology Counteracts Androgenic Hair Loss and Promotes Hair Growth in A Blinded Clinical Trial.
J Clin Med
January 2023
Department of Hand, Plastic, Reconstructive and Burn Surgery, BG-Unfallklinik Tuebingen, University of Tuebingen, Schnarrenbergstraße 95, 72076 Tübingen, Germany.
Androgenic alopecia (AGA) is a genetically predetermined condition that occurs as a result of stepwise miniaturization of the dermal papilla. During this process, the hair follicle suffers from increasing malnutrition and eventually dies, causing progressive hair loss. We recently highlighted that HIF-1α modulation may counteract hair loss.
View Article and Find Full Text PDFDTP-Net: A convolutional neural network model to predict threshold for localizing the lesions on dermatological macro-images.
Comput Biol Med
September 2022
Department of Electronics and Communication Engineering, National Institute of Technology Puducherry, Karaikal, Puducherry - 609609, India. Electronic address:
Highly focused images of skin captured with ordinary cameras, called macro-images, are extensively used in dermatology. Being highly focused views, the macro-images contain only lesions and background regions. Hence, the localization of lesions on the macro-images is a simple thresholding problem.
View Article and Find Full Text PDFAn EfficientNet-based modified sigmoid transform for enhancing dermatological macro-images of melanoma and nevi skin lesions.
Comput Methods Programs Biomed
July 2022
Department of Electronics and Communication Engineering, National Institute of Technology Puducherry, Karaikal, Puducherry 609609, India. Electronic address:
Background And Objective: During the initial stages, skin lesions may not have sufficient intensity difference or contrast from the background region on dermatological macro-images. The lack of proper light exposure at the time of capturing the image also reduces the contrast. Low contrast between lesion and background regions adversely impacts segmentation.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!