Skin disease analysis and tracking based on image segmentation

Trabelsi, Olfa; Tlig, Lotfi; Sayadi, Mounir; Fnaiech, Farhat

doi:10.1109/iceesa.2013.6578486

Cited by 16 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Trabelsi et al [17] used a variety of clustering techniques, including fuzzy c-means, modified fuzzy cmeans, and K-means, to segment a skin disease with an 83% true positive rate. The clustering algorithms depend on the identification of a centroid that can generalise a cluster of data.…”

Section: Literature Reviewmentioning

confidence: 99%

An automatic feature extraction technique from the images of granular parakeratosis disease

Janthakal¹,

Hosalli²

2022

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

The largest and most vital part of the human body is skin and any change in the features of skin is termed as a skin lesion. The paper considers granular parakeratosis lesion that is an epidermal reaction occurring due to the disorder of keratinization, and mainly seen in intertriginous areas. The manual inspection of the lesion features is a bit cumbersome due to which an automated system is proposed in this paper. The main goal is to determine the size and depth of granular parakeratosis lesions using the proposed ensemble algorithm, partition clustering and region properties method. As a flow of the proposed model, segmentation is done using U-net with binary cross entropy, features are extracted using partition clustering and region properties method, and classification is done using SVM 10-fold model. The proposed feature extraction method estimates the depth and absolute size of K lesions in each image by predicting the absolute height and width of the lesion in terms of pixel square. After extracting the features, classification is done, thereby obtaining an accuracy of 95%, sensitivity and specificity of 100%. The proposed model provides better performance compared to state-of-the-art models. The main application of this automated system is in dermatology field where some skin lesions have same features which makes the experts to diagnose the disease incorrectly. If the proposed system is incorporated, diagnosis can be done in an effective manner considering all the relevant features.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

An automatic feature extraction technique from the images of granular parakeratosis disease

Janthakal¹,

Hosalli²

2022

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

show abstract

“…The skin disease detection system (Figure 1) consists of two main stages, such as the image processing stage and the machine learning stage (Roy, 2019;Trabelsi, 2013). In the image processing stage, the dermoscopy or sample images (Figure 2) are first taken into the system as input.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…This stage tries to segment out the region of interest (ROI) in dermoscopy images (Chowdhury, 2016;George, 2016;Manoorkar, 2016;Trabelsi, 2013;Roy, 2019). There are diverse segmentation techniques, such as color-based segmentation, texture-based segmentation, Otsu thresholding, fuzzy C-means clustering, K-means clustering, morphological operations, background subtraction, region growing, edge-based segmentation, gradient vector-based segmentation, active contour model (ACM), Bi-level thresholding, and HED (holistically-nested edge detection) used in dermoscopy images for extraction of ROI.…”

Section: Image Segmentationmentioning

confidence: 99%

A Survey on a Skin Disease Detection System

Mamun

Uddin

2021

International Journal of Healthcare Information Systems and Informatics

View full text Add to dashboard Cite

Skin diseases are frequent and quite perennial in the world, and in some cases, these lead to cancer. These are curable if detected earlier and treated appropriately. An automated image-based detection system consisting of four main modules: image enhancement, region of interest segmentation, feature extraction, and detection can facilitate early identification of these diseases. Diverse image-based methods incorporating machine learning techniques are developed to diagnose different types of skin diseases. This article focuses on the review of the tools and techniques used in the diagnosis of 28 common skin diseases. Furthermore, it has discussed the available image databases and the evaluation metrics for the performance analysis of various diagnosis systems. This is vital for figuring out the implementation framework as well as the efficacy of the diagnosis methods for the neophyte. Based on the performance accuracy, the state-of-the-art method for the diagnosis of a particular disease is figured out. It also highlights challenges and shows future research directions.

show abstract

“…Table 1 gives a summary of the contributions in the field of pressure injuries segmentation and measurement. Some of the related works were able to measure the segmentation area of the wound using real-world units [ 28 , 29 , 30 ]. However, bypassing the 3D information of the wounds results in biased values.…”

Section: Related Workmentioning

confidence: 99%

Integrating 3D Model Representation for an Accurate Non-Invasive Assessment of Pressure Injuries with Deep Learning

Zahia

Garcia‐Zapirain

Elmaghraby

2020

Sensors

View full text Add to dashboard Cite

Pressure injuries represent a major concern in many nations. These wounds result from prolonged pressure on the skin, which mainly occur among elderly and disabled patients. If retrieving quantitative information using invasive methods is the most used method, it causes significant pain and discomfort to the patients and may also increase the risk of infections. Hence, developing non-intrusive methods for the assessment of pressure injuries would represent a highly useful tool for caregivers and a relief for patients. Traditional methods rely on findings retrieved solely from 2D images. Thus, bypassing the 3D information deriving from the deep and irregular shape of this type of wounds leads to biased measurements. In this paper, we propose an end-to-end system which uses a single 2D image and a 3D mesh of the pressure injury, acquired using the Structure Sensor, and outputs all the necessary findings such as: external segmentation of the wound as well as its real-world measurements (depth, area, volume, major axis and minor axis). More specifically, a first block composed of a Mask RCNN model uses the 2D image to output the segmentation of the external boundaries of the wound. Then, a second block matches the 2D and 3D views to segment the wound in the 3D mesh using the segmentation output and generates the aforementioned real-world measurements. Experimental results showed that the proposed framework can not only output refined segmentation with 87% precision, but also retrieves reliable measurements, which can be used for medical assessment and healing evaluation of pressure injuries.

show abstract

Skin disease analysis and tracking based on image segmentation

Cited by 16 publications

References 22 publications

An automatic feature extraction technique from the images of granular parakeratosis disease

An automatic feature extraction technique from the images of granular parakeratosis disease

A Survey on a Skin Disease Detection System

Integrating 3D Model Representation for an Accurate Non-Invasive Assessment of Pressure Injuries with Deep Learning

Contact Info

Product

Resources

About