This work will describe the challenges involved in setting up automatic processing for a large differentiated data set. In this study, a multispectral (skin diffuse reflection images using 526nm (green), 663nm (red), and 964nm (infrared) illumination and autofluorescence (AF) image using 405 nm excitation) data set with 756 lesions (3024 images) was processed. Previously, using MATLAB software, finding markers, correctly segmenting images with dark edges and image alignment were the main causes of the problems in automatic data processing. To improve automatic processing and eliminate the use of licensed software, the latter was substituted with the open source Python environment. For more precise segmentation of skin markers and skin lesions, as well for image alignment, the processing of artificial neural networks was utilized. The resulting processing method solves most of the issues of the MATLAB script. However, for even more accurate results, it is necessary to provide more accurate ground-truth segmentation masks and generate more input data to increase the training image database by using data augmentation.
Skin cancer is the most common type of cancer in the USA and worldwide.1 An early diagnosis is the key to a successful treatment. Among the skin cancers, the malignant melanoma (MM) accounts for 1% of the cases while it is responsible for the majority of deaths. Basal cell carcinoma (BCC) is the most common form of skin cancer with a very low mortality rate.2 Unfortunately, skin cancer recurrence is a common problem for MM and BCC patients. We propose a post-operative scar screening with non-invasive autofluorescence (AF) imaging to detect an early growth of any residual tissue from the cancer removal procedure. The screening images can serve also as a visual evidence for the post-op patient’s observation in dynamics. The results of the study show promising results comparing various post-op scars with recurrent cancer cases.
The aim of this study is to develop a novel non-invasive approach for skin cancer (melanoma, basal cell and squamous cell carcinomas) diagnostics by mapping the AF intensity decrease (photo-bleaching) rates under continuous 405 nm LED excitation. For parametric mapping of skin AF intensity decrease rates a sequence of filtered AF imaging under 405 nm LED excitation for 20 seconds at a power density of ~7 mW/cm2 with a frame rate 0.5 fps was recorded and analyzed by cloud-based prototype device. Several clinical cases and potential future applications of the proposed autofluorescence photobleaching rate imaging technique are discussed.
Skin cancer is the most common type of malignant tumors in humans. Early diagnosis is the key to successful surgical treatment. In this work we present a non-invasive screening tool for early stage detection of skin cancer and also for the evaluation of post-operative scars.
This paper presents the results of statistical clinical data, combining two diagnostic methods. A combination of two skin imaging methods – diffuse reflectance and autofluorescence – has been applied for skin cancer diagnostics. Autofluorescence (AF) and multispectral diffuse reflectance images were acquired by custom made prototype with 405 nm, 526 nm, 663 nm and 964 nm LEDs and RGB CMOS camera. Parameter p’ was calculated from diffuse reflectance images under green, red and infrared illumination, AF intensity (I’) was calculated from AF images exited at 405nm wavelength. Obtained results show that criterion p` > 1 gives possibility to discriminate melanomas and different kind of keratosis from other lesions, and criterion I` < 0.2 gives the possibility to discriminate melanomas from keratosis.
Multispectral diffuse reflectance imaging and autofluorescence photo-bleaching imaging are methods that have been investigated for use in skin disorder diagnostics. In response to the ever-increasing incidence of skin cancer in light skinned populations a new device has been designed incorporating both of these methods. The aim of the study was to create a device that is most efficient in terms of hardware and software parameters for the screening of malignant and benign skin lesions. A set of 525 nm, 630 nm and 980 nm LEDs were used to illuminate the skin area at three wavelengths [1] and a set of 405 nm LEDs were used to induce the skin autofluorescence [2]. For a more homogenous illumination of investigated skin area the optimal placement for LEDs in a cylindrical case was found. The requisite spacing from the camera lens was taken into account to produce a focused RGB image. The geometrical shape of the device allows to capture images of skin that are illuminated solely by the diodes without interference from sunlight or other nearby light sources. Polarizing filters were used to decrease glare effects, therefore preventing image overexposure of very reflective skin areas. 515 nm long pass filter was used to enable the 405 nm excitation while capturing autofluorescence images of the skin. Further improvements to the quality of the diagnostic data can be achieved using reference images to track homogeneity of the intensity and then applying a compensating algorithm on the subsequent screening images. These and other design considerations serve to realize the full potential of the diagnostic method.
Results of clinical approbation to assess the efficacy of the new device to diagnose malignant skin lesions will be demonstrated.
As the incidence of skin cancer is still increasing worldwide, there is a high demand for early, non-invasive and inexpensive skin lesion diagnostics. In this article we describe and combine two skin imaging methods: skin autofluorescence (AF) and multispectral criterion p’. To develop this method, we used custom made prototype with 405 nm, 526 nm, 663 nm and 964 nm LED illuminations, perpendicular positioned linear polarizers, 515 nm filter and IDS camera. Our aim is to develop a skin lesion diagnostic device for primary care physicians who do not have experience in dermatology or skin oncology. In this study we included such common benign lesion groups as seborrheic keratosis, hyperkeratosis, melanocytic nevi and hemangiomas, as well two types of skin cancers: basal cell carcinoma and melanoma. By combining skin AF and multispectral p’ imaging methods, we achieved 100% sensitivity and 100% specificity for distinguishing melanoma (3 histologically confirmed cases) from seborrheic keratosis (13 dermatologically confirmed cases), hyperkeratosis (8 histologically and 1 dermatologically confirmed case), melanocytic nevi (23 dermatologically confirmed cases ), basal cell carcinomas (2 histologically and 16 dermatologically confirmed cases) and hemangiomas (8 dermatologically confirmed cases). Unfortunately, currently this method cannot distinguish the basal cell carcinoma group from benign lesion groups.
A clinical trial on autofluorescence imaging of malignant and non-malignant skin pathologies comprising 32 basal cell carcinomas (BCC), 4 malignant melanomas (MM), 1 squamous cell carcinoma (SCC), 89 nevi, 14 dysplastic nevi, 20 hemangiomas, 23 seborrheic keratoses, 4 hyperkeratoses, 3 actinic keratoses, 3 psoriasis, 1 dematitis, 2 dermatofibromas, 5 papillofibromas, 12 lupus erythematosus, 7 purpura, 6 bruises, 5 freckles, 3 fungal infections, 1 burn, 1 tattoo, 1 age spot, 1 vitiligo, 32 postoperative scars, 8 post cream therapy BCCs, 4 post radiation therapy scars, 2 post laser therapy scars, 1 post freezing scar as well as 114 reference images of healthy skin was performed. The sequence of autofluorescence images of skin pathologies were recorded by smartphone RGB camera under continuous 405 nm LED excitation during 20 seconds with 0.5 fps. Obtained image sequences further were processed with subsequent extraction of autofluorescence intensity and photobleaching parameters.
The feasibility of smartphones for in vivo skin autofluorescence imaging has been investigated. Filtered autofluorescence images from the same tissue area were periodically captured by a smartphone RGB camera with subsequent detection of fluorescence intensity decreasing at each image pixel for further imaging the planar distribution of those values. The proposed methodology was tested clinically with 13 basal cell carcinoma and 1 atypical nevus. Several clinical cases and potential future applications of the smartphone-based technique are discussed.
Begin basal cell carcinoma (BCC) is most common skin cancer over the world. There are around 20 modalities for BCC treatment. Laser surgery is uncommon option. We demonstrate our long term follow up results. Aim: To evaluate long term efficacy of a 980nm diode laser for the difficult-to-treat basal cell carcinoma. Materials and Methods: 167 patients with 173 basal cell carcinoma on the nose were treated with a 980 nm diode laser from May 1999 till May 2005 at Latvian Oncology center. All tumors were morphologically confirmed. 156 patients were followed for more than 5 years. Results: The lowest recurrence rate was observed in cases of superficial BCC, diameter<6mm; bet the highest recurrence rate was in cases of infiltrative BCC and nodular recurrent BCC. Conclusions: 980 nm diode laser is useful tool in dermatology with high long term efficacy, good acceptance by the patients and good cosmetics results.
Noninvasive multispectral imaging method was applied for different skin pathology such as nevus, basal cell carcinoma,
and melanoma diagnostics. Developed melanoma diagnostic parameter, using three spectral bands (540 nm, 650 nm and
950 nm), was calculated for nevus, melanoma and basal cell carcinoma. Simple multispectral diagnostic device was
established and applied for skin assessment. Development and application of multispectral diagnostics method described
further in this article.
A clinical trial involving multi-spectral imaging of histologically confirmed 8 basaliomas and 30 melanomas was
performed. Parametric maps of the melanin index, erythema index and melanoma-nevus differentiation parameter have
been constructed and mutually compared. Specific features of basalioma and melanoma images were analyzed and
discussed.
A clinical trial on multi-spectral imaging of malignant and non-malignant skin pathologies comprising 22
melanomas and 59 pigmented nevi was performed in Latvian Oncology Center. Analysis of data obtained in the
spectral range 450-950 nm using multispectral camera have led to a novel image processing algorithm capable
to distinguish melanoma from pigmented nevi and different areas of activity of melanoma. The proposed
methodology and potential clinical applications are discussed.
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