Dr. Robert W. LeAnder Profile

Dr. Robert W. LeAnder

Professor at Southern Illinois Univ Edwardsville

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Publications (5)

Proceedings Article | 27 September 2016 Presentation + Paper

Comparison of algorithms for automatic border detection of melanoma in dermoscopy images

Sowmya Srinivasa Raghavan, Ravneet Kaur, Robert LeAnder

Proceedings Volume 9971, 997105 (2016) https://doi.org/10.1117/12.2237064

KEYWORDS: Melanoma, Detection and tracking algorithms, Image segmentation, Algorithm development, Cancer, RGB color model, Photomasks, Skin, Image processing, Image processing algorithms and systems

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Proceedings Article | 27 September 2016 Presentation + Paper

An effective hair detection algorithm for dermoscopic melanoma images of skin lesions

Damayanti Chakraborti, Ravneet Kaur, Scott Umbaugh, Robert LeAnder

Proceedings Volume 9971, 997104 (2016) https://doi.org/10.1117/12.2236565

KEYWORDS: Skin, Melanoma, Detection and tracking algorithms, Edge detection, Image segmentation, Digital filtering, Algorithm development, Image filtering, Sensors, Image processing algorithms and systems

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Proceedings Article | 9 March 2010 Paper

New algorithm for detecting smaller retinal blood vessels in fundus images

Robert LeAnder, Praveen Bidari, Tauseef Mohammed, Moumita Das, Scott Umbaugh

Proceedings Volume 7624, 76243B (2010) https://doi.org/10.1117/12.844996

KEYWORDS: Image segmentation, Blood vessels, Hough transforms, Image processing algorithms and systems, Signal to noise ratio, Databases, Algorithm development, Binary data, Image filtering, Spatial filters

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Proceedings Article | 27 March 2008 Paper

Comparison of two algorithms in the automatic segmentation of blood vessels in fundus images

Robert LeAnder, Myneni Sushma Chowdary, Swapnasri Mokkapati, Scott Umbaugh

Proceedings Volume 6915, 69153H (2008) https://doi.org/10.1117/12.770786

KEYWORDS: Image segmentation, Signal to noise ratio, Blood vessels, Image processing algorithms and systems, Detection and tracking algorithms, Binary data, Image filtering, Edge detection, Databases, Hough transforms

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Effective timing and treatment are critical to saving the sight of patients with diabetes. Lack of screening, as well as a shortage of ophthalmologists, help contribute to approximately 8,000 cases per year of people who lose their sight to diabetic retinopathy, the leading cause of new cases of blindness [1] [2]. Timely treatment for diabetic retinopathy prevents severe vision loss in over 50% of eyes tested [1]. Fundus images can provide information for detecting and monitoring eye-related diseases, like diabetic retinopathy, which if detected early, may help prevent vision loss. Damaged blood vessels can indicate the presence of diabetic retinopathy [9]. So, early detection of damaged vessels in retinal images can provide valuable information about the presence of disease, thereby helping to prevent vision loss. Purpose: The purpose of this study was to compare the effectiveness of two blood vessel segmentation algorithms. Methods: Fifteen fundus images from the STARE database were used to develop two algorithms using the CVIPtools software environment. Another set of fifteen images were derived from the first fifteen and contained ophthalmologists' hand-drawn tracings over the retinal vessels. The ophthalmologists' tracings were used as the "gold standard" for perfect segmentation and compared with the segmented images that were output by the two algorithms. Comparisons between the segmented and the hand-drawn images were made using Pratt's Figure of Merit (FOM), Signal-to-Noise Ratio (SNR) and Root Mean Square (RMS) Error. Results: Algorithm 2 has an FOM that is 10% higher than Algorithm 1. Algorithm 2 has a 6%-higher SNR than Algorithm 1. Algorithm 2 has only 1.3% more RMS error than Algorithm 1. Conclusions: Algorithm 1 extracted most of the blood vessels with some missing intersections and bifurcations. Algorithm 2 extracted all the major blood vessels, but eradicated some vessels as well. Algorithm 2 outperformed Algorithm 1 in terms of visual clarity, FOM and SNR. The performances of these algorithms show that they have an appreciable amount of potential in helping ophthalmologists detect the severity of eye-related diseases and prevent vision loss.

Proceedings Article | 30 March 2007 Paper

Automatic differentiation of melanoma and Clark nevus skin lesions

R. LeAnder, A. Kasture, A. Pandey, S. Umbaugh

Proceedings Volume 6514, 65142V (2007) https://doi.org/10.1117/12.709986

KEYWORDS: Melanoma, Tumors, Image segmentation, Skin, Image classification, Photography, Image processing, Image filtering, Feature extraction, Skin cancer

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Skin cancer is the most common form of cancer in the United States. Although melanoma accounts for just 11% of all types of skin cancer, it is responsible for most of the deaths, claiming more than 7910 lives annually. Melanoma is visually difficult for clinicians to differentiate from Clark nevus lesions which are benign. The application of pattern recognition techniques to these lesions may be useful as an educational tool for teaching physicians to differentiate lesions, as well as for contributing information about the essential optical characteristics that identify them. Purpose: This study sought to find the most effective features to extract from melanoma, melanoma in situ and Clark nevus lesions, and to find the most effective pattern-classification criteria and algorithms for differentiating those lesions, using the Computer Vision and Image Processing Tools (CVIPtools) software package. Methods: Due to changes in ambient lighting during the photographic process, color differences between images can occur. These differences were minimized by capturing dermoscopic images instead of photographic images. Differences in skin color between patients were minimized via image color normalization, by converting original color images to relative-color images. Relative-color images also helped minimize changes in color that occur due to changes in the photographic and digitization processes. Tumors in the relative-color images were segmented and morphologically filtered. Filtered, relative-color, tumor features were then extracted and various pattern-classification schemes were applied. Results: Experimentation resulted in four useful pattern classification methods, the best of which was an overall classification rate of 100% for melanoma and melanoma in situ (grouped) and 60% for Clark nevus. Conclusion: Melanoma and melanoma in situ have feature parameters and feature values that are similar enough to be considered one class of tumor that significantly differs from Clark nevus. Consequently, grouping melanoma and melanoma in situ together achieves the best results in classifying and automatically differentiating melanoma from Clark nevus lesions.

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