Haemoglobin distribution in ulcers for healing assessment

Hani, Ahmad Fadzil Mohamad; Arshad, Leena; Malik, Aamir Saeed; Jamil, Adawiyah; Bin, Felix Yap Boon

doi:10.1109/icias.2012.6306219

Cited by 17 publications

(13 citation statements)

References 14 publications

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“…Our results demonstrate that the probability map approach computed through a YC b C r colour space is a successful method to eliminate the effect from light source and skin colour during PI segmentation. Unlike the method used in previous studies (Alves et al 2001, Wannous et al 2007, Veredas et al 2010, Hani et al 2012, Hettiarachchi et al 2013, our method will work with PI images captured from real clinical environment. In addition, our method do not require (1) the clinician to trace the edge of the PI in the picture, (2) the clinician to manually remove the clinical background objects in the picture, (3) any special adjustment for light conditions and the skin colour of patients, and (4) the camera to be oriented perpendicularly to the PI.…”

Section: Resultsmentioning

confidence: 99%

“…, Hani et al . ). Moreover, none of these studies took into account how the camera was not always oriented perpendicularly to the plane of the PI, which results in a photograph that does not reflect the true shape of the PI.…”

Section: Introductionmentioning

confidence: 97%

“…, Hani et al . , Hettiarachchi et al . ), most of the work in this area deals with images that involve strictly controlled image conditions (Wannous et al .…”

Section: Introductionmentioning

confidence: 99%

“…Although PI segmentation from photographic images has been the subject of several previous studies (e.g. Alves et al 2001, Wannous et al 2007, Veredas et al 2010, Hani et al 2012, Hettiarachchi et al 2013, most of the work in this area deals with images that involve strictly controlled image conditions (Wannous et al 2007, Hettiarachchi et al 2013 or is confined to the wound region only (Alves et al 2001, Veredas et al 2010, Hani et al 2012. Moreover, none of these studies took into account how the camera was not always oriented perpendicularly to the plane of the PI, which results in a photograph that does not reflect the true shape of the PI.…”

Section: Introductionmentioning

confidence: 99%

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Automated measurement of pressure injury through image processing

Mathews

2017

Journal of Clinical Nursing

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

“…, Hani et al . , Hettiarachchi et al . ), most of the work in this area deals with images that involve strictly controlled image conditions (Wannous et al .…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated measurement of pressure injury through image processing

Mathews

2017

Journal of Clinical Nursing

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“…It achieves 71.4% accuracy (MLP kernel) and 85.7% accuracy (RBF kernel); Wantanajittikul et al [15] apply FCM & morphology, texture analysis and SVM to do image segmentation and characterization for 5 images (burn cases). It achieves 72.0–98.0% accuracy; Hani et al [16] apply ICA and k-means to do granulation detection and segmentation for 30 wound region images. It achieves 88.2% sensitivity 98.8% specificity; Veredas et al [17] apply mean shift & region growing to do wound segmentation and tissue characterization for 113 wound region images; Hettiarachchi et al [11] apply active contour to do wound segmentation for 20 wound region images under controlled conditions, it achieves 90.0% accuracy; Wannous et al [18] apply mean shift, JSEG, CSC & SVM to do wound segmentation for 25 images with background under controlled conditions.…”

Section: Methodsmentioning

confidence: 99%

Chronic wound assessment and infection detection method

Hsu

Chen

et al. 2019

BMC Med Inform Decis Mak

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Background Numerous patients suffer from chronic wounds and wound infections nowadays. Until now, the care for wounds after surgery still remain a tedious and challenging work for the medical personnel and patients. As a result, with the help of the hand-held mobile devices, there is high demand for the development of a series of algorithms and related methods for wound infection early detection and wound self monitoring. Methods This research proposed an automated way to perform (1) wound image segmentation and (2) wound infection assessment after surgical operations. The first part describes an edge-based self-adaptive threshold detection image segmentation method to exclude nonwounded areas from the original images. The second part describes a wound infection assessment method based on machine learning approach. In this method, the extraction of feature points from the suture area and an optimal clustering method based on unimodal Rosin threshold algorithm that divides feature points into clusters are introduced. These clusters are then merged into several regions of interest (ROIs), each of which is regarded as a suture site. Notably, a support vector machine (SVM) can automatically interpret infections on these detected suture site. Results For (1) wound image segmentation, boundary-based evaluation were applied on 100 images with gold standard set up by three physicians. Overall, it achieves 76.44% true positive rate and 89.04% accuracy value. For (2) wound infection assessment, the results from a retrospective study using confirmed wound pictures from three physicians for the following four symptoms are presented: (1) Swelling, (2) Granulation, (3) Infection, and (4) Tissue Necrosis. Through cross-validation of 134 wound images, for anomaly detection, our classifiers achieved 87.31% accuracy value; for symptom assessment, our classifiers achieved 83.58% accuracy value. Conclusions This augmentation mechanism has been demonstrated reliable enough to reduce the need for face-to-face diagnoses. To facilitate the use of this method and analytical framework, an automatic wound interpretation app and an accompanying website were developed. Trial registration 201505164RIND , 201803108RSB .

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Mobile Apps for Wound Assessment and Monitoring: Limitations, Advancements and Opportunities

Kabir,

Samad,

Ahmed

et al. 2024

J Med Syst

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With the proliferation of wound assessment apps across various app stores and the increasing integration of artificial intelligence (AI) in healthcare apps, there is a growing need for a comprehensive evaluation system. Current apps lack sufficient evidence-based reliability, prompting the necessity for a systematic assessment. The objectives of this study are to evaluate the wound assessment and monitoring apps, identify limitations, and outline opportunities for future app development. An electronic search across two major app stores (Google Play store, and Apple App Store) was conducted and the selected apps were rated by three independent raters. A total of 170 apps were discovered, and 10 were selected for review based on a set of inclusion and exclusion criteria. By modifying existing scales, an app rating scale for wound assessment apps is created and used to evaluate the selected ten apps. Our rating scale evaluates apps’ functionality and software quality characteristics. Most apps in the app stores, according to our evaluation, do not meet the overall requirements for wound monitoring and assessment. All the apps that we reviewed are focused on practitioners and doctors. According to our evaluation, the appImitoWoundgot the highest mean score of 4.24. But this app has 7 criteria among our 11 functionalities criteria. Finally, we have recommended future opportunities to leverage advanced techniques, particularly those involving artificial intelligence, to enhance the functionality and efficacy of wound assessment apps. This research serves as a valuable resource for future developers and researchers seeking to enhance the design of wound assessment-based applications, encompassing improvements in both software quality and functionality.

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Haemoglobin distribution in ulcers for healing assessment

Cited by 17 publications

References 14 publications

Automated measurement of pressure injury through image processing

Automated measurement of pressure injury through image processing

Chronic wound assessment and infection detection method

Mobile Apps for Wound Assessment and Monitoring: Limitations, Advancements and Opportunities

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