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- Dermoscopy (2)
- Melanoma (2)
- Segmentation (2)
- Cervical cancer (1)
- Cervical intraepithelial neoplasia (1)
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- Classification (1)
- Convolutional neural networks (1)
- Deep learning (1)
- Detection (1)
- Digital pathology (1)
- Hair removal (1)
- Histology (1)
- Image processing (1)
- Image segmentation (1)
- Inpainting (1)
- Mathematical morphology (1)
- Milia-like Cyst (1)
- Noisy data (1)
- Seborrheic Keratosis (1)
- Whole slide image (1)
Articles 1 - 5 of 5
Full-Text Articles in Dermatology
Sharprazor: Automatic Removal Of Hair And Ruler Marks From Dermoscopy Images, Reda Kasmi, Jason Hagerty, Reagan Harris Young, Norsang Lama, Januka Nepal, Jessica Miinch, William V. Stoecker, R. Joe Stanley
Sharprazor: Automatic Removal Of Hair And Ruler Marks From Dermoscopy Images, Reda Kasmi, Jason Hagerty, Reagan Harris Young, Norsang Lama, Januka Nepal, Jessica Miinch, William V. Stoecker, R. Joe Stanley
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Background: The removal of hair and ruler marks is critical in handcrafted image analysis of dermoscopic skin lesions. No other dermoscopic artifacts cause more problems in segmentation and structure detection. Purpose: The aim of the work is to detect both white and black hair, artifacts and finally inpaint correctly the image. Method: We introduce a new algorithm: SharpRazor, to detect hair and ruler marks and remove them from the image. Our multiple-filter approach detects hairs of varying widths within varying backgrounds, while avoiding detection of vessels and bubbles. The proposed algorithm utilizes grayscale plane modification, hair enhancement, segmentation using tri-directional …
Skin Lesion Segmentation In Dermoscopic Images With Noisy Data, Norsang Lama, Jason Hagerty, Anand Nambisan, Ronald Joe Stanley, William Van Stoecker
Skin Lesion Segmentation In Dermoscopic Images With Noisy Data, Norsang Lama, Jason Hagerty, Anand Nambisan, Ronald Joe Stanley, William Van Stoecker
Electrical and Computer Engineering Faculty Research & Creative Works
We Propose a Deep Learning Approach to Segment the Skin Lesion in Dermoscopic Images. the Proposed Network Architecture Uses a Pretrained Efficient Net Model in the Encoder and Squeeze-And-Excitation Residual Structures in the Decoder. We Applied This Approach on the Publicly Available International Skin Imaging Collaboration (ISIC) 2017 Challenge Skin Lesion Segmentation Dataset. This Benchmark Dataset Has Been Widely Used in Previous Studies. We Observed Many Inaccurate or Noisy Ground Truth Labels. to Reduce Noisy Data, We Manually Sorted All Ground Truth Labels into Three Categories — Good, Mildly Noisy, and Noisy Labels. Furthermore, We Investigated the Effect of Such …
Automated Cervical Digitized Histology Whole-Slide Image Analysis Toolbox, Sudhir Sornapudi, Ravitej Addanki, R. Joe Stanley, William V. Stoecker, Rodney Long, Rosemary Zuna, Shellaine R. Frazier, Sameer Antani
Automated Cervical Digitized Histology Whole-Slide Image Analysis Toolbox, Sudhir Sornapudi, Ravitej Addanki, R. Joe Stanley, William V. Stoecker, Rodney Long, Rosemary Zuna, Shellaine R. Frazier, Sameer Antani
Electrical and Computer Engineering Faculty Research & Creative Works
Background: Cervical intraepithelial neoplasia (CIN) is regarded as a potential precancerous state of the uterine cervix. Timely and appropriate early treatment of CIN can help reduce cervical cancer mortality. Accurate estimation of CIN grade correlated with human papillomavirus type, which is the primary cause of the disease, helps determine the patient's risk for developing the disease. Colposcopy is used to select women for biopsy. Expert pathologists examine the biopsied cervical epithelial tissue under a microscope. The examination can take a long time and is prone to error and often results in high inter-and intra-observer variability in outcomes. Methodology: We propose …
Posterolateral Neck Texture (Insulin Neck): Early Sign Of Insulin Resistance, Katie S. Payne, Ryan K. Rader, Guido Lastra, William V. Stoecker
Posterolateral Neck Texture (Insulin Neck): Early Sign Of Insulin Resistance, Katie S. Payne, Ryan K. Rader, Guido Lastra, William V. Stoecker
Chemistry Faculty Research & Creative Works
No abstract provided.
Cloudy And Starry Milia-Like Cysts: How Well Do They Distinguish Seborrheic Keratoses From Malignant Melanomas?, S. M. Stricklin, William V. Stoecker, M. C. Oliviero, H. S. Rabinovitz, S. K. Mahajan
Cloudy And Starry Milia-Like Cysts: How Well Do They Distinguish Seborrheic Keratoses From Malignant Melanomas?, S. M. Stricklin, William V. Stoecker, M. C. Oliviero, H. S. Rabinovitz, S. K. Mahajan
Chemistry Faculty Research & Creative Works
Background Seborrheic keratoses are the most common skin lesions known to contain small white or yellow structures called milia-like cysts (MLCs). Varied appearances can sometimes make it difficult to differentiate benign lesions from malignant lesions such as melanoma, the deadliest form of skin cancer found in humans. Objective the purpose of this study was to determine the statistical occurrence of MLCs in benign vs. malignant lesions. Methods a medical student with 10 months experience in examining approximately 1000 dermoscopy images and a dermoscopy-naïve observer analyzed contact non-polarized dermoscopy images of 221 malignant melanomas and 175 seborrheic keratoses for presence of …