scholarly journals Semi-automatic Segmentation of Skin Lesions based on Superpixels and Hybrid Texture Information

2022 ◽  
pp. 102363
Author(s):  
Elineide S. dos Santos ◽  
Rodrigo de M.S. Veras ◽  
Kelson R.T. Aires ◽  
Helano M.B.F. Portela ◽  
Geraldo Braz Junior ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Skin Lesions ◽  
Texture Information

A Study on Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images

2015 ◽  
Vol 6 (4) ◽  
pp. 51-61
Author(s):  
Ebtihal Abdullah Al-Mansour ◽  
Arfan Jaffar
Keyword(s):  
Skin Cancer ◽  
Imaging Techniques ◽  
Automatic Segmentation ◽  
Survival Rates ◽  
Skin Lesions ◽  
Incidence Rates ◽  
Automated System ◽  
Final State ◽  
Survey Paper ◽  
Real Scenario

Malignant Melanoma is one of the rare and the deadliest form of skin cancer if left untreated. Death rate due to this cancer is three times more than all other skin-related malignancies combined. Incidence rates of melanoma have been increasing, especially among young adults, but survival rates are high if detected early. There is a need for an automated system to assess a patient's risk of melanoma using digital dermoscopy, that is, a skin imaging technique widely used for pigmented skin lesion inspection. Although many automated and semi-automated methods are available to diagnose skin cancer but each has its own limitations and there is no final, state-of-the art technique to date which is able to be implemented in real scenario. This survey paper is based on techniques used to segment the skin cancer, analysis of their merits and demerits and their applications on advanced imaging techniques.

scholarly journals Improved diagnosis and navigation for CT colonography

2021 ◽  
Author(s):  
Jinjie Ming
Keyword(s):  
Ct Colonography ◽  
Automatic Segmentation ◽  
Segmentation Method ◽  
Polyp Detection ◽  
Data Set ◽  
Colon Wall ◽  
Texture Information ◽  
Ct Data ◽  
New Feature ◽  
Shape And Size

This project describes the development of an automatic segmentation method and a novel navigation system that detect polyps using advanced image processing and computer graphics tecniques. The colon wall segmentation method from the CT data set of abdomen is achieved by combining the contouring model - level set method and the minima detection using mathematical morphology theory. Polyp detection is attained by analyzing surface curvature and texture information along on the colon wall. Adding texture analysis provides a new feature for improving currently existing methods. As such, polyp candidates are examined not only by their shape and size but also by their texture appearance.

scholarly journals Improved diagnosis and navigation for CT colonography

2021 ◽  
Author(s):  
Jinjie Ming
Keyword(s):  
Ct Colonography ◽  
Automatic Segmentation ◽  
Segmentation Method ◽  
Polyp Detection ◽  
Data Set ◽  
Colon Wall ◽  
Texture Information ◽  
Ct Data ◽  
New Feature ◽  
Shape And Size

This project describes the development of an automatic segmentation method and a novel navigation system that detect polyps using advanced image processing and computer graphics tecniques. The colon wall segmentation method from the CT data set of abdomen is achieved by combining the contouring model - level set method and the minima detection using mathematical morphology theory. Polyp detection is attained by analyzing surface curvature and texture information along on the colon wall. Adding texture analysis provides a new feature for improving currently existing methods. As such, polyp candidates are examined not only by their shape and size but also by their texture appearance.

Deep Learning-Based Automated Mole Detection: An Algorithm with Future Implication for Tele-dermatology (Preprint)

2020 ◽  
Author(s):  
Quoc-Viet Tran ◽  
Yen-Po Chin ◽  
Phung-Anh Nguyen ◽  
Ming-Yang Lee ◽  
Hsuan-Chia Yang ◽  
...  
Keyword(s):  
Public Health ◽  
Deep Learning ◽  
Automatic Segmentation ◽  
Confidence Score ◽  
Skin Lesions ◽  
Average Precision ◽  
Health Events ◽  
Bounding Boxes ◽  
Future Implication ◽  
Deep Learning Model

BACKGROUND The automatic segmentation of skin lesions has been reported using the data of dermoscopic images. It is, however, not applicable to real-time detection using a smartphone. OBJECTIVE This study aims to examine a deep learning model for detecting and localizing positions of the mole on the captured images to precisely extract the crop images of the model without any other objects. METHODS The data were collected through public health events in Taiwan between December 2017 and February 2019. All the participants who concerned about the risk of their moles were asked to take the mole-images. Images were then measured and determined the risks by three dermatologists. We labeled the mole position with bounding boxes using the ‘LabelImg’ tool. Two architectures, SSD and Faster-RCNN, have been used to build eight different mole-detection models. The confidence score, intersection over union (IoU), and mean average precision (mAP) with the COCO metrics were used to measure the accuracy of those models. RESULTS 2790-mole images were used for the development and the validation of the models. The Faster-RCNN Inception Resnet model had the highest overall mAP of 0.245, following by 0.234 of the Faster-RCNN Resnet 101, and 0.227 of the Faster-RCNN Resnet 50 model. The SSD Mobilenet v1 model had the lowest mAP of 0.142. The Faster-RCNN Inception Resnet model had a dominant AP of 0.377, 0.236, and 0.129 for the large, medium, and small size of moles. We observed that the Faster RCNN Inception Resnet has shown the best performance with the high confident scores (over 97%) for all kinds of moles. CONCLUSIONS We successfully developed the detection models based on the techniques of SSD and Faster-RCNN. These models might help researchers to localize accurately the position of the moles with its risks as a feasible detection app on the smartphone. We provided the pre-trained models for further studies via GitHub link, https://github.com/vietdaica/Mole_Detection.

scholarly journals Automatic Segmentation of Dermoscopic Images by Iterative Classification

2011 ◽  
Vol 2011 ◽  
pp. 1-19 ◽  
Author(s):  
Maciel Zortea ◽  
Stein Olav Skrøvseth ◽  
Thomas R. Schopf ◽  
Herbert M. Kirchesch ◽  
Fred Godtliebsen
Keyword(s):  
Binary Classification ◽  
Automatic Segmentation ◽  
Classification Problem ◽  
Skin Lesions ◽  
Diagnostic Systems ◽  
Training Samples ◽  
Hybrid Classification ◽  
Binary Classification Problem ◽  
Segmentation Problem ◽  
Weighted Combination

Accurate detection of the borders of skin lesions is a vital first step for computer aided diagnostic systems. This paper presents a novel automatic approach to segmentation of skin lesions that is particularly suitable for analysis of dermoscopic images. Assumptions about the image acquisition, in particular, the approximate location and color, are used to derive an automatic rule to select small seed regions, likely to correspond to samples of skin and the lesion of interest. The seed regions are used as initial training samples, and the lesion segmentation problem is treated as binary classification problem. An iterative hybrid classification strategy, based on a weighted combination of estimated posteriors of a linear and quadratic classifier, is used to update both the automatically selected training samples and the segmentation, increasing reliability and final accuracy, especially for those challenging images, where the contrast between the background skin and lesion is low.

scholarly journals Attention-Guided Network with Densely Connected Convolution for Skin Lesion Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3462
Author(s):  
Shengxin Tao ◽  
Yun Jiang ◽  
Simin Cao ◽  
Chao Wu ◽  
Zeqi Ma
Keyword(s):  
Survival Rate ◽  
Skin Lesion ◽  
Automatic Segmentation ◽  
Skin Lesions ◽  
Diagnosis And Treatment ◽  
Guided Filter ◽  
Lesion Segmentation ◽  
Data Set ◽  
Low Contrast

The automatic segmentation of skin lesions is considered to be a key step in the diagnosis and treatment of skin lesions, which is essential to improve the survival rate of patients. However, due to the low contrast, the texture and boundary are difficult to distinguish, which makes the accurate segmentation of skin lesions challenging. To cope with these challenges, this paper proposes an attention-guided network with densely connected convolution for skin lesion segmentation, called CSAG and DCCNet. In the last step of the encoding path, the model uses densely connected convolution to replace the ordinary convolutional layer. A novel attention-oriented filter module called Channel Spatial Fast Attention-guided Filter (CSFAG for short) was designed and embedded in the skip connection of the CSAG and DCCNet. On the ISIC-2017 data set, a large number of ablation experiments have verified the superiority and robustness of the CSFAG module and Densely Connected Convolution. The segmentation performance of CSAG and DCCNet is compared with other latest algorithms, and very competitive results have been achieved in all indicators. The robustness and cross-data set performance of our method was tested on another publicly available data set PH2, further verifying the effectiveness of the model.

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