Pattern Recognition of Lower Member Skin Ulcers in Medical Images with Machine Learning Algorithms

Seixas, José Luis; Barbon, Sylvio; Mantovani, Rafael Gomes

doi:10.1109/cbms.2015.48

Cited by 17 publications

(10 citation statements)

References 8 publications

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“…can be automatically evaluated by Computer-Aided Diagnosis (CAD) tools, or even used for the searching of massive databases through content-only queries, as in Content-Based Image Retrieval (CBIR) applications. In both CAD and CBIR cases, the detection of abnormalities requires the extraction of patterns from images, while a decision-making strategy is necessary for juxtaposing new images to those in the database [4,5].Since dermatological lesions are routinely diagnosed by biopsies and surrounding skin aspects, ulcers can be computationally characterized by particular types of tissues (and their areas) within the wounded region [6,7]. For instance, Mukherjee et al[8] proposed a five-color classification model and applied a color-based low-level extractor further labeled by a Support-Vector Machine (SVM) strategy at an 87.61% hit ratio.…”

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confidence: 99%

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A superpixel-driven deep learning approach for the analysis of dermatological wounds

Blanco¹,

Traina²,

Traina³

et al. 2020

Computer Methods and Programs in Biomedicine

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Background. The image-based identification of distinct tissues within dermatological wounds enhances patients' care since it requires no intrusive evaluations. This manuscript presents an approach, we named QTDU , that combines deep learning models with superpixel-driven segmentation methods for assessing the quality of tissues from dermatological ulcers. Method. QTDU consists of a three-stage pipeline for the obtaining of ulcer segmentation, tissues' labeling, and wounded area quantification. We set up our approach by using a real and annotated set of dermatological ulcers for training several deep learning models to the identification of ulcered superpixels. Results. Empirical evaluations on 179,572 superpixels divided into four classes showed QTDU accurately spot wounded tissues (AUC = 0.986, sensitivity = 0.97, and specificity = 0.974) and outperformed machinelearning approaches in up to 8.2% regarding F1-Score through fine-tuning of a ResNet-based model. Last, but not least, experimental evaluations also showed QTDU correctly quantified wounded tissue areas within a 0.089 Mean Absolute Error ratio. Conclusions. Results indicate QTDU effectiveness for both tissue segmentation and wounded area quantification tasks. When compared to existing machine-learning approaches, the combination of superpixels and deep learning models outperformed the competitors within strong significant levels. can be automatically evaluated by Computer-Aided Diagnosis (CAD) tools, or even used for the searching of massive databases through content-only queries, as in Content-Based Image Retrieval (CBIR) applications. In both CAD and CBIR cases, the detection of abnormalities requires the extraction of patterns from images, while a decision-making strategy is necessary for juxtaposing new images to those in the database [4,5].Since dermatological lesions are routinely diagnosed by biopsies and surrounding skin aspects, ulcers can be computationally characterized by particular types of tissues (and their areas) within the wounded region [6,7]. For instance, Mukherjee et al.[8] proposed a five-color classification model and applied a color-based low-level extractor further labeled by a Support-Vector Machine (SVM) strategy at an 87.61% hit ratio. Such idea of concatenating feature extraction and classification is found at the core of most wound segmentation strategies, as in the study of Kavitha et al.[9] that evaluated leg ulcerations by extracting patterns based on local spectral histograms to be labeled by a Multi-Layer Perceptron (MLP) classifier with 87.05% accuracy. Analogously, Pereyra et al.[10] discussed the use of color descriptors and an Instancebased Learning (IbL) classifier with a 61.7% hit ratio, whereas Veredas et al. [11] suggested the use of texture descriptors and an MLP classifier with 84.84% accuracy.Blanco et al.[4] and Chino et al.[12] followed a slightly different premise for finding proper similarity measures and comparison criteria for dermatological wounds. Their approaches are based on a divide-andconquer stra...

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confidence: 99%

“…Since dermatological lesions are routinely diagnosed by biopsies and surrounding skin aspects, ulcers can be computationally characterized by particular types of tissues (and their areas) within the wounded region [6,7]. For instance, Mukherjee et al…”

mentioning

confidence: 99%

A superpixel-driven deep learning approach for the analysis of dermatological wounds

Blanco¹,

Traina²,

Traina³

et al. 2020

Computer Methods and Programs in Biomedicine

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“…In addition, traditional machine learning methods were used to perform wound segmentation. Both unsupervised methods such as clustering [ 20 , 21 ], and supervised methods such as Support Vector Machines (SVM) [ 22 , 23 ] and Bayesian classifiers [ 24 ] were used to segment the wound after extracting the features such as mean-color information, color histogram statistics and Scale Invariant Feature Transform (SIFT) features [ 25 ]. The issue with traditional approaches is the necessity to choose the features which are important in the given images.…”

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

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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.

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“…One problem of this method is the need for a controlled environment. Another skin ulcer segmentation method was proposed by Seixas et al (SEIXAS;BARBON;MANTOVANI, 2015). Seixas et al employed off-the-shelf classifiers to segment ulcer images.…”

Section: Venous Skin Ulcersmentioning

confidence: 99%

“…However, accurate and automatic lesions measurement strongly relies on wellsegmented regions. Existing works lack on accurate segmentation, as they focus more in the retrieval and classification tasks (DORILEO et al, 2010;PEREYRA et al, 2014;SEIXAS;BARBON;MANTOVANI, 2015;BLANCO et al, 2016;.…”

Section: Introductionmentioning

confidence: 99%

Managing feature extraction, mining and retrieval of complex data: applications in emergency situations and medicine

Chino¹

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The size and complexity of the data generated by social media and medical images has increased in a fast pace. Unlike traditional data, images cannot be dealt with in its original domain, leading to rising challenges in knowledge discovery tasks. The image analysis can aid on several decision making tasks. Crowdsourcing images such as social media images can be used to increase the speed of authorities to take action in emergency situations. Images taken from the medical domain can support on daily activities of physicians to diagnose their patients. Content-Based Image Retrieval (CBIR) systems are built to retrieve similar images, being an important step for the knowledge discovery. However, in some image domains, only parts of the image are relevant to the problem. This PhD research is based on the following hypothesis: the integration of image segmentation methods with local feature CBIR system improves the precision of the retrieved images. We evaluate our proposals in two images domain: fire detection on urban emergency situations and chronic skin ulcer images. The main contributions of this PhD research can be divided in four parts. First, we propose BoWFire to detect and segment fire in emergency situations. We explore the combination of color and texture features through superpixels to detect fire in still images. Then, we explore the use of superpixels to extract local features with BoSS. BoSS is a Bag-of-Visual-Words (BoVW) approach based on visual signatures. To integrate segmentation methods with CBIR, we propose ICARUS, a skin ulcer image retrieval framework. ICARUS integrate segmentations methods based on superpixels with BoVW. We also propose ASURA, a deep learning segmentation method for skin ulcer lesions. Besides segmenting skin ulcer lesions, ASURA is able to estimate the area of the lesion in real-world units by analyzing real-world objects present in the images. Our experiments show that our proposals achieved a better precision while retrieving the most similar images in comparison with the existing approaches.

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Pattern Recognition of Lower Member Skin Ulcers in Medical Images with Machine Learning Algorithms

Cited by 17 publications

References 8 publications

A superpixel-driven deep learning approach for the analysis of dermatological wounds

A superpixel-driven deep learning approach for the analysis of dermatological wounds

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

Managing feature extraction, mining and retrieval of complex data: applications in emergency situations and medicine

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