Lacunarity Analysis: A Promising Method for the Automated Assessment of Melanocytic Naevi and Melanoma

The early diagnosis of melanoma is critical to achieving reduced mortality and increased survival. Although clinical examination is currently the method of choice for melanocytic lesion assessment, there is a growing interest among clinicians regarding the potential diagnostic utility of computerised image analysis. Recognising that there exist significant shortcomings in currently available algorithms, we are motivated to investigate the utility of lacunarity, a simple statistical measure previously used in geology and other fields for the analysis of fractal and multi-scaled images, in the automated assessment of melanocytic naevi and melanoma. Digitised dermoscopic images of 111 benign melanocytic naevi, 99 dysplastic naevi and 102 melanomas were obtained over the period 2003 to 2008, and subject to lacunarity analysis. We found the lacunarity algorithm could accurately distinguish melanoma from benign melanocytic naevi or non-melanoma without introducing many of the limitations associated with other previously reported diagnostic algorithms. Lacunarity analysis suggests an ordering of irregularity in melanocytic lesions, and we suggest the clinical application of this ordering may have utility in the naked-eye dermoscopic diagnosis of early melanoma

Application of Statistical Indicators for Digital Image Analysis and Segmentation in Sorting of Agriculture Products

Food processing industry is moving forward to a full automation of all processes, especially in technological line segments which represent critical control points of food safety. One of these points is color sorting by using machine vision, where inappropriate products are removed. Most important product appearance attributes are color and texture. During food processing, the product is captured by optical devices, mostly color cameras and lasers. The aim of this paper is to investigate new eligibility criteria for digital image segmentation by using only image from the camera. The goal is to describe the texture of the product, based on chosen mathematical measures, and to allow for recognition and then classification according to the predefined range of values in an appropriate class. Images of frozen raspberry were used. Image analysis of color parameters in RGB color space and statistical tests to examine normality of data were carried out. Thereafter, one-way Anova and correlation analysis was performed. Statistically significant difference was found for the values of two indicators: entropy and new criteria were derived from standard deviation, as well as mean values of pixels for every channel, and marked as L. After determining the range of these criteria, a new algorithm was developed for image segmentation written in Matlab. One of the results of applying this algorithm is that more than 80% of good products were recognized

Octave-spanning broadband absorption of terahertz light using metasurface fractal-cross absorbers

Synthetic fractals inherently carry spatially encoded frequency information that renders them as an ideal candidate for broadband optical structures. Nowhere is this more true than in the terahertz (THz) band where there is a lack of naturally occurring materials with valuable optical properties. One example are perfect absorbers that are a direct step toward the development of highly sought after detectors and sensing devices. Metasurface absorbers that can be used to substitute for natural materials suffer from poor broadband performance, while those with high absorption and broadband capability typically involve complex fabrication and design and are multilayered. Here, we demonstrate a polarization-insensitive ultrathin (∼λ/6) planar metasurface THz absorber composed of supercells of fractal crosses capable of spanning one optical octave in bandwidth, while still being highly efficient. A sufficiently thick polyimide interlayer produces a unique absorption mechanism based on Salisbury screen and antireflection responses, which lends to the broadband operation. Experimental peak absorption exceeds 93%, while the average absorption is 83% from 2.82 THz to 5.15 THz. This new ultrathin device architecture, achieving an absorption-bandwidth of one optical octave, demonstrates a major advance toward a synthetic metasurface blackbody absorber in the THz ban

A Review on Skin Disease Classification and Detection Using Deep Learning Techniques

Skin cancer ranks among the most dangerous cancers. Skin cancers are commonly referred to as Melanoma. Melanoma is brought on by genetic faults or mutations on the skin, which are caused by Unrepaired Deoxyribonucleic Acid (DNA) in skin cells. It is essential to detect skin cancer in its infancy phase since it is more curable in its initial phases. Skin cancer typically progresses to other regions of the body. Owing to the disease's increased frequency, high mortality rate, and prohibitively high cost of medical treatments, early diagnosis of skin cancer signs is crucial. Due to the fact that how hazardous these disorders are, scholars have developed a number of early-detection techniques for melanoma. Lesion characteristics such as symmetry, colour, size, shape, and others are often utilised to detect skin cancer and distinguish benign skin cancer from melanoma. An in-depth investigation of deep learning techniques for melanoma's early detection is provided in this study. This study discusses the traditional feature extraction-based machine learning approaches for the segmentation and classification of skin lesions. Comparison-oriented research has been conducted to demonstrate the significance of various deep learning-based segmentation and classification approaches

Vascular patterning of subcutaneous mouse fibrosarcomas expressing individual VEGF isoforms can be differentiated using angiographic optical coherence tomography

Subcutaneously implanted experimental tumors in mice are commonly used in cancer research. Despite their superficial location, they remain a challenge to image noninvasively at sufficient spatial resolution for microvascular studies. Here we evaluate the capabilities of optical coherence tomography (OCT) angiography for imaging such tumors directly through the murine skin in-vivo. Datasets were collected from mouse tumors derived from fibrosarcoma cells genetically engineered to express only single splice variant isoforms of vascular endothelial growth factor A (VEGF); either VEGF120 or VEGF188 (fs120 and fs188 tumors respectively). Measured vessel diameter was found to be significantly (p<0.001) higher for fs120 tumors (60.7±4.9μm) compared to fs188 tumors (45.0±4.0μm). The fs120 tumors also displayed significantly higher vessel tortuosity, fractal dimension and density. The ability to differentiate between tumor types with OCT suggests that the visible abnormal vasculature is representative of the tumor microcirculation, providing a robust, non-invasive method for observing the longitudinal dynamics of the subcutaneous tumor microcirculation