Hyperspectral index-based metric for burn depth assessment

Parasca, Sorin Viorel; Manea, Dragoș; Micloş, S.; Savastru, Roxana

doi:10.1364/boe.9.005778

Cited by 14 publications

(11 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Paluchowski et al [6] tested HSI on pig burns of various severity together with a spectral-spatial unsupervised segmentation method obtaining maps that differentiated among burns with different depths. Parasca et al [7] developed and tested a skin burn spectral index and compared it with laser Doppler imaging with good results. Marotz et al [8] developed a hyperspectral imaging system and provided a 3D burn perfusion characterization by analyzing multiple parameters.…”

Section: Introductionmentioning

confidence: 99%

Burn characterization using object‐oriented hyperspectral image classification

Parasca

2022

Journal of Biophotonics

Self Cite

View full text Add to dashboard Cite

This paper presents a new approach based on hyperspectral imaging combined with an object‐oriented classification method that allows the generation of burn depth classification maps facilitating easier characterization of burns. Hyperspectral images of 14 patients diagnosed with burns on the upper and lower limbs were acquired using a pushbroom hyperspectral imaging system. The images were analyzed using an object‐oriented classification approach that uses objects with specific spectral, textural and spatial attributes as the minimum unit for classifying information. The method performance was evaluated in terms of overall accuracy, sensitivity, precision and specificity computed from the confusion matrix. The results revealed that the approach proposed in this study performed well in differentiating burn classes with a high level of overall accuracy (95.99% ± 0.60%), precision (97.30% ± 2.46%), sensitivity (97.23% ± 3.02%) and specificity (98.02% ± 1.98%). In conclusion, the object‐based approach for burns hyperspectral images classification can provide maps that can help surgeons identify with better precision different depths of burn wounds.

show abstract

Section: Introductionmentioning

confidence: 99%

Burn characterization using object‐oriented hyperspectral image classification

Parasca

2022

Journal of Biophotonics

Self Cite

View full text Add to dashboard Cite

show abstract

“…With that, clinicians and researchers, alike, are making continuous efforts to both improve diagnostic accuracy and reduce patient suffering. Currently, several techniques, including photoacoustic imaging (PAI) [2,3], hyperspectral imaging [4,5], and infrared thermal imaging [6], have been developed and tested for burn injury assessment, but none have become commonplace within the clinic because of various technical drawbacks and commercialization issues [7]. A number of other techniques, however, such as laser speckle contrast imaging (LSCI) and laser doppler flowmetry (LDF) have been commercialized and subsequently used to assess burn-damaged skin.…”

Section: Introductionmentioning

confidence: 99%

Application of OCT‐Derived Attenuation Coefficient in Acute Burn‐Damaged Skin

Deegan

Cheng

et al. 2021

Lasers Surg Med

View full text Add to dashboard Cite

Background and Objectives: There remains a need to objectively monitor burn wound healing within a clinical setting, and optical coherence tomography (OCT) is proving itself one of the ideal modalities for just such a use. The aim of this study is to utilize the noninvasive and multipurpose capabilities of OCT, along with its cellularlevel resolution, to demonstrate the application of optical attenuation coefficient (OAC), as derived from OCT data, to facilitate the automatic digital segmentation of the epidermis from scan images and to work as an objective indicator for burn wound healing assessment. Study Design/Materials and Methods: A simple, yet efficient, method was used to estimate OAC from OCT images taken over multiple time points following acute burn injury. This method enhanced dermal-epidermal junction (DEJ) contrast, which facilitated the automatic segmentation of the epidermis for subsequent thickness measurements. In addition, we also measured and compared the average OAC of the dermis within said burns for correlative purposes. Results: Compared with unaltered OCT maps, enhanced DEJ contrast was shown in OAC maps, both from single A-lines and completed B-frames. En face epidermal thickness and dermal OAC maps both demonstrated significant changes between imaging sessions following burn injury, such as a loss of epidermal texture and decreased OAC. Quantitative analysis also showed that OAC of acute burned skin decreased below that of healthy skin following injury. Conclusions: Our study has demonstrated that the OAC estimated from OCT data can be used to enhance imaging contrast to facilitate the automatic segmentation of the epidermal layer, as well as help elucidate our understanding of the pathological changes that occur in human skin when exposed to acute burn injury, which could serve as an objective indicator of skin injury and healing.

show abstract

“…HSI is a method imported from the remote sensing domain and gave promising experimental results in several medical areas including cancer detection [11][12][13], mapping skin burn [14], chronic skin ulcer diagnosis [15], kidney stone type characterization [16], peripheral vascular disease screening [17] or tissue blood oxygenation monitoring [18,19]. The method provides the reflectance spectra of hundreds of pixels in the investigated area, giving also spatial information.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of basal cell carcinoma by hyperspectral imaging

Parasca

2021

Journal of Biophotonics

Self Cite

View full text Add to dashboard Cite

The purpose of this study was to test the ability of hyperspectral imaging (HSI) combined with unsupervised anomaly detectors to automatically differentiate basal cell carcinoma (BCC) from normal skin. Hyperspectral images of the face of a female patient with a BCC of the lower lip were acquired using a visible/near-infrared HSI system and two anomaly detection algorithms (Reed-Xiaoli and Reed-Xiaoli/Uniform Target hybrid anomaly detectors) were used to detect pathological tissue from normal skin. The results revealed that the receiver operating characteristic curve of the Reed-Xiaoli/Uniform Target hybrid detector was higher than that of the Reed-Xiaoli detector in the range of false positive rates between 0 and 0.8. The area under curve values were good (0.7074 and 0.8607, respectively) with Reed-Xiaoli/Uniform Target hybrid detector performing better. In conclusion, HSI combined with either of two anomaly detectors can play a promising role in the automated screening of BCC.

show abstract

Hyperspectral index-based metric for burn depth assessment

Cited by 14 publications

References 35 publications

Burn characterization using object‐oriented hyperspectral image classification

Burn characterization using object‐oriented hyperspectral image classification

Application of OCT‐Derived Attenuation Coefficient in Acute Burn‐Damaged Skin

Automatic detection of basal cell carcinoma by hyperspectral imaging

Contact Info

Product

Resources

About