Engineering Commons^™

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 …

Deep Learning Nuclei Detection In Digitized Histology Images By Superpixels, Sudhir Sornapudi, R. Joe Stanley, William V. Stoecker, Haidar Almubarak, Rodney Long, Sameer Antani, George Thoma, Rosemary Zuna, Shelliane R. Frazier

Electrical and Computer Engineering Faculty Research & Creative Works

Background: Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades.

Methods: In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network.

Results: The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with …

Convolutional Neural Network Based Localized Classification Of Uterine Cervical Cancer Digital Histology Images, Haidar A. Almubarak, R. Joe Stanley, Rodney Long, Sameer Antani, George Thoma, Rosemary Zuna, Shelliane R. Frazier

Electrical and Computer Engineering Faculty Research & Creative Works

In previous research, we introduced an automated localized, fusion-based algorithm to classify squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN). The approach partitioned the epithelium into 10 segments. Image processing and machine vision algorithms were used to extract features from each segment. The features were then used to classify the segment and the result was fused to classify the whole epithelium. This research extends the previous research by dividing each of the 10 segments into 3 parts and uses a convolutional neural network to classify the 3 parts. The result is then fused to …

Cervical Cancer Histology Image Feature Extraction And Classification, Peng Guo

Masters Theses

"Cervical cancer, the second most common cancer affecting women worldwide and the most common in developing countries can be cured if detected early and treated. Expert pathologists routinely visually examine histology slides for cervix tissue abnormality assessment. In previous research, an automated, localized, fusion-based approach was investigated for classifying squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) based on image analysis of 62 digitized histology images obtained through the National Library of Medicine. In this research, CIN grade assessments from two pathologists are analyzed and are used to facilitate atypical cell concentration feature development …

Feature Extraction Through Median-Split Algorithm Segmentation For Melanoma Detection, Venkata Sai Narasimha Kaushik Ghantasala

Masters Theses

""Detection of melanoma remains an empirical clinical science. New tools for automatic discrimination of melanoma from benign lesions in digitized dermoscopy images may allow an improvement in early detection of melanoma. This research implements a fast version of the median split algorithm in an open source format and applied to four-color splitting of the lesion area to capture the architectural disorder apparent in melanoma colors. This version of the median split algorithm splits colors along the color axis with maximum range". For a dermoscopy set of 888 images, K-means clustering algorithm is compared with a median split algorithm to find …

Feature Extraction Through K-Means Segmentation For Melanoma Detection, Snigdha Priya Bommadevara

Masters Theses

"Malignant melanoma is responsible for 75% of the deaths caused due to skin cancer annually. However, melanoma detection can be possible through feature extraction and pattern classification, which can lower the risk, if the melanoma is detected at an early stage. Clustering is one of the most useful tools used to differentiate features that can contribute to melanoma. This research work uses the k-means clustering algorithm for implementation of color segmentation. However, k-means clustering requires a predefined value of k, i.e., the number of clusters must be specified at the beginning of the run. This research uses a predefined value …

Graphical Image Classification Combining An Evolutionary Algorithm And Binary Particle Swarm Optimization, Beibei Cheng, Renzhong Wang, Sameer K. Antani, R. Joe Stanley, George R. Thoma

Electrical and Computer Engineering Faculty Research & Creative Works

Biomedical journal articles contain a variety of image types that can be broadly classified into two categories: regular images, and graphical images. Graphical images can be further classified into four classes: diagrams, statistical figures, flow charts, and tables. Automatic figure type identification is an important step toward improved multimodal (text + image) information retrieval and clinical decision support applications. This paper describes a feature-based learning approach to automatically identify these four graphical figure types. We apply Evolutionary Algorithm (EA), Binary Particle Swarm Optimization (BPSO) and a hybrid of EA and BPSO (EABPSO) methods to select an optimal subset of extracted …

Live Wire Segmentation Tool For Osteophyte Detection In Lumbar Spine X-Ray Images, Santosh Seetharaman, R. Joe Stanley, Soumya De, Sameer Antani, L. Rodney Long, George R. Thoma

Electrical and Computer Engineering Faculty Research & Creative Works

Computer-assisted vertebra segmentation in x-ray images is a challenging problem. Inter-subject variability and the generally poor contrast of digitized radiograph images contribute to the segmentation difficulty. In this paper, a semi-automated live wire approach is investigated for vertebrae segmentation. The live wire approach integrates initially selected user points with dynamic programming to generate a closed vertebra boundary. In order to assess the degree to which vertebra features are conserved using the live wire technique, convex hull-based features to characterize anterior osteophytes in lumbar vertebrae are determined for live wire and manually segmented vertebrae. Anterior osteophyte discrimination was performed over 405 …

Interactive Virtual Laboratory For Experience With A Smart Bridge Test, Elizabeth C. Eckhoff, Vicki M. Eller, Steve Eugene Watkins, Richard Hall

Electrical and Computer Engineering Faculty Research & Creative Works

Virtual laboratory experiments can be cost effective, convenient instructional resources that have appeal to a wide range of learning styles. Expensive, time-consuming laboratory tests can be experienced repeatedly and remotely using interactive simulations and original video footage or animations. A virtual experiment can incorporate meaningful exercises, procedural options, and background hyperlinks to create a comprehensive "hands on" environment. Also, it may be used as preliminary training for the actual experiment.

An interactive LabVIEW-based laboratory for a load test simulation of an existing demonstration bridge was created. This smart truss bridge is instrumented with fiber optic strain sensors situated on the …