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Dive into the research topics where Robert W. LeAnder is active.

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Featured researches published by Robert W. LeAnder.


Education and Urban Society | 2010

Morale of Teachers in High Poverty Schools: A Post-NCLB Mixed Methods Analysis.

Marie Byrd-Blake; Michael O. Afolayan; John W. Hunt; Martins Fabunmi; Brandt W. Pryor; Robert W. LeAnder

This study tested how well Fishbein and Ajzen’s Theory of Reasoned Action predicted the attitudes and morale of urban teachers in high poverty schools under the pressures of the No Child Left Behind Act (NCLB). NCLB forced local administrators to target schools that had not made adequately yearly progress (AYP) for two or more consecutive years. Teachers from 4 schools in an urban school district in Southern Illinois were surveyed under the scope of the theory of reasoned action. Quantitative and qualitative results were analyzed to determine that the pressure of NCLB adversely affected teachers’ morale.


Skin Research and Technology | 2010

Differentiation of melanoma from benign mimics using the relative-color method.

Robert W. LeAnder; Prathibha Chindam; Moumita Das; Scott E. Umbaugh

Background: Previous studies have successfully classified 86% of malignant melanomas using a relative‐color segmentation method, by feature extraction from photographic images in the automatic identification of skin tumors. These studies were extended by applying the relative‐color method to dermoscopic images of melanoma grouped with melanoma in situ and clark nevus lesions in dermoscopic images allow more control over lighting variations, which contribute to lesion misclassification. Dermoscopic images then enable a more detailed examination of the structure of skin lesions, provide much more structural detail within lesions, and contain visual information that cannot be seen in photographic images. This present work extends the previous studies by applying relative‐color feature extraction to dermoscopic images to differentiate among melanoma, seborrheic keratoses and Reed/Spitz nevi.


Skin Research and Technology | 2012

DISCRIMINATION OF BASAL CELL CARCINOMA FROM BENIGN LESIONS BASED ON EXTRACTION OF ULCER FEATURES IN POLARIZED-LIGHT DERMOSCOPY IMAGES

Serkan Kefel; Pelin Guvenc; Robert W. LeAnder; Sherea Stricklin; William V. Stoecker

Ulcers are frequently visible in magnified, cross‐polarized, dermoscopy images of basal cell carcinoma. An ulcer without a history of trauma, a so‐called ‘atraumatic’ ulcer, is an important sign of basal cell carcinoma, the most common skin cancer. Distinguishing such ulcers from similar features found in benign lesions is challenging. In this research, color and texture features of ulcers are analyzed to discriminate basal cell carcinoma from benign lesions.


Proceedings of SPIE | 2016

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

Damayanti Chakraborti; Ravneet Kaur; Scott E. Umbaugh; Robert W. LeAnder

Dermoscopic images are obtained using the method of skin surface microscopy. Pigmented skin lesions are evaluated in terms of texture features such as color and structure. Artifacts, such as hairs, bubbles, black frames, ruler-marks, etc., create obstacles that prevent accurate detection of skin lesions by both clinicians and computer-aided diagnosis. In this article, we propose a new algorithm for the automated detection of hairs, using an adaptive, Canny edge-detection method, followed by morphological filtering and an arithmetic addition operation. The algorithm was applied to 50 dermoscopic melanoma images. In order to ascertain this method’s relative detection accuracy, it was compared to the Razmjooy hair-detection method [1], using segmentation error (SE), true detection rate (TDR) and false positioning rate (FPR). The new method produced 6.57% SE, 96.28% TDR and 3.47% FPR, compared to 15.751% SE, 86.29% TDR and 11.74% FPR produced by the Razmjooy method [1]. Because of the 7.27-9.99% improvement in those parameters, we conclude that the new algorithm produces much better results for detecting thick, thin, dark and light hairs. The new method proposed here, shows an appreciable difference in the rate of detecting bubbles, as well.


Skin Research and Technology | 2013

Sector expansion and elliptical modeling of blue-gray ovoids for basal cell carcinoma discrimination in dermoscopy images

Pelin Guvenc; Robert W. LeAnder; Serkan Kefel; William V. Stoecker; Ryan K. Rader; Kristen A. Hinton; Sherea Stricklin; Harold S. Rabinovitz; Margaret Oliviero; Randy H. Moss

Blue‐gray ovoids (B‐GOs), a critical dermoscopic structure for basal cell carcinoma (BCC), offer an opportunity for automatic detection of BCC. Due to variation in size and color, B‐GOs can be easily mistaken for similar structures in benign lesions. Analysis of these structures could afford accurate characterization and automatic recognition of B‐GOs, furthering the goal of automatic BCC detection. This study utilizes a novel segmentation method to discriminate B‐GOs from their benign mimics.


Medical Imaging 2007: Computer-Aided Diagnosis | 2007

Automatic differentiation of melanoma and clark nevus skin lesions

Robert W. LeAnder; A. Kasture; A. Pandey; Scott E. Umbaugh

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.


international conference on computer vision theory and applications | 2017

Automatic Separation of Basal Cell Carcinoma from Benign Lesions in Dermoscopy Images with Border Thresholding Techniques.

Nabin K. Mishra; Ravneet Kaur; Reda Kasmi; Serkan Kefel; Pelin Guvenc; Justin G. Cole; Jason R. Hagerty; Hemanth Y. Aradhyula; Robert W. LeAnder; R. Joe Stanley; Randy H. Moss; William V. Stoecker

Basal cell carcinoma (BCC), with an incidence in the US exceeding 2.7 million cases/year, exacts a significant toll in morbidity and financial costs. Earlier BCC detection via automatic analysis of dermoscopy images could reduce the need for advanced surgery. In this paper, automatic diagnostic algorithms are applied to images segmented by five thresholding segmentation routines. Experimental results for five new thresholding routines are compared to expert-determined borders. Logistic regression analysis shows that thresholding segmentation techniques yield diagnostic accuracy that is comparable to that obtained with manual borders. The experimental results obtained with algorithms applied to automatically segmented lesions demonstrate significant potential for the new machine vision techniques.


Skin Research and Technology | 2017

THRESHOLDING METHODS FOR LESION SEGMENTATION OF BASAL CELL CARCINOMA IN DERMOSCOPY IMAGES

R. Kaur; Robert W. LeAnder; Nabin K. Mishra; Jason R. Hagerty; R. Kasmi; Ronald Joe Stanley; M. E. Celebi; William V. Stoecker

Algorithms employed for pigmented lesion segmentation perform poorly on dermoscopy images of basal cell carcinoma (BCC), the most common skin cancer. The main objective was to develop better methods for BCC segmentation.


Proceedings of SPIE | 2016

Comparison of Algorithms for Automatic Border Detection of Melanoma in Dermoscopy Images

Sowmya Srinivasa Raghavan; Ravneet Kaur; Robert W. LeAnder

Melanoma is one of the most rapidly accelerating cancers in the world [1]. Early diagnosis is critical to an effective cure. We propose a new algorithm for more accurately detecting melanoma borders in dermoscopy images. Proper border detection requires eliminating occlusions like hair and bubbles by processing the original image. The preprocessing step involves transforming the RGB image to the CIE L*u*v* color space, in order to decouple brightness from color information, then increasing contrast, using contrast-limited adaptive histogram equalization (CLAHE), followed by artifacts removal using a Gaussian filter. After preprocessing, the Chen-Vese technique segments the preprocessed images to create a lesion mask which undergoes a morphological closing operation. Next, the largest central blob in the lesion is detected, after which, the blob is dilated to generate an image output mask. Finally, the automatically-generated mask is compared to the manual mask by calculating the XOR error [3]. Our border detection algorithm was developed using training and test sets of 30 and 20 images, respectively. This detection method was compared to the SRM method [4] by calculating the average XOR error for each of the two algorithms. Average error for test images was 0.10, using the new algorithm, and 0.99, using SRM method. In comparing the average error values produced by the two algorithms, it is evident that the average XOR error for our technique is lower than the SRM method, thereby implying that the new algorithm detects borders of melanomas more accurately than the SRM algorithm.


Skin Research and Technology | 2013

Region growing by sector analysis for detection of blue‐gray ovoids in basal cell carcinoma

S. Pelin Guvenc; Robert W. LeAnder; Serkan Kefel; Ryan K. Rader; Kristen A. Hinton; Sherea Stricklin; William V. Stoecker

Blue‐gray ovoids (B‐GOs) are critical dermoscopic structures in basal cell carcinomas (BCCs) that pose a challenge for automatic detection. Due to variation in size and color, B‐GOs can be easily mistaken for similar structures in benign lesions. Analysis of these structures could help further accomplish the goal of automatic BCC detection. This study introduces an efficient sector‐based method for segmenting B‐GOs. Four modifications of conventional region‐growing techniques are presented: (i) employing a seed area rather than a seed point, (ii) utilizing fixed control limits determined from the seed area to eliminate re‐calculations of previously‐added regions, (iii) determining region growing criteria using logistic regression, and (iv) area analysis and expansion by sectors. Contact dermoscopy images of 68 confirmed BCCs having B‐GOs were obtained. A total of 24 color features were analyzed for all B‐GO seed areas. Logistic regression analysis determined blue chromaticity, followed by red variance, were the best features for discriminating B‐GO edges from surrounding areas. Segmentation of malignant structures obtained an average Pratts figure of merit of 0.397. The techniques presented here provide a non‐recursive, sector‐based, region‐growing method applicable to any colored structure appearing in digital images. Further research using these techniques could lead to automatic detection of B‐GOs in BCCs.

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William V. Stoecker

Missouri University of Science and Technology

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Scott E. Umbaugh

Southern Illinois University Edwardsville

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Serkan Kefel

Southern Illinois University Edwardsville

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Pelin Guvenc

Southern Illinois University Edwardsville

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Ravneet Kaur

Southern Illinois University Edwardsville

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Moumita Das

Southern Illinois University Edwardsville

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Nabin K. Mishra

Missouri University of Science and Technology

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R. Joe Stanley

Missouri University of Science and Technology

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