Multispectral Computer Vision Research Articles

Diagnostic performance of the MelaFind device in a real-life clinical setting.

MelaFind is a multispectral computer vision system intended to -provide additional information on melanocytic lesions suspected of being melanoma by -objectively assessing their three-dimensional morphology. Analysis of the diagnostic performance of MelaFind in a real-life clinical setting. In this observational study, 360 pigmented skin lesions (PSL) in 111 patients were assessed by office-based dermatologists using MelaFind. Scores ≥2 were considered to be suspicious of malignancy. The decision for surgical excision was left to the discretion of the examining dermatologists. MelaFind scores ≥2 were observed in 147 of 360 PSL (40.8%). Of the 107 excised lesions with a MelaFind-score ≥2, the diagnosis of melanoma was made in three cases; 53 (49.5%) lesions proved to be dysplastic nevi. Among all lesions biopsied (n = 113), the sensitivity and specificity of MelaFind was 100% and 5.5%, respectively. While a higher specificity of 68.5% may be assumed with respect to the overall data set (n = 360), this assumption is limited by incomplete follow-up data required to confirm that all non-excised lesions with a score <2 were actually benign. The high sensitivity of MelaFind facilitated the detection of melanoma. The overall specificity and benign-to-malignant ratio of excised lesions were acceptable. These parameters may be improved by using higher cutoff scores for excisional biopsies, and by more vigorously selecting PSL for MelaFind examination.

A real-time multispectral computer vision system for morpho-thermal analysis of footprint plantar

Physical Rehabilitation processes in amputees, especially those immersed in post-conflict policies of the Colombian government, requires, among other things, a full comprehensive biomechanical and clinical study of high quality. Generally, this comprehensive study includes kinetic, kinematic, dynamic analysis, electromyography and thermal and morphological podiatric assessment. Research and technological development around the first four tests have been rapidly so hardware and software tools are flooding the current market. However, podiatric assessment has been a little behind in this aspect, since many clinics and rehabilitation centers prefer conventional methods for footprint plantar analysis, which are invasive, slow, tedious, subjective and poor in visualization. In this work, two new methodologies for obtaining thermal and morphological parameters of the footprint plantar from thermopedographies and podoscopies are proposed respectively. To achieve this, common elements in algorithms for footprint plantar analysis based on image are identified and computer vision techniques are proposed in each case to automate and systematize the whole process. As a result, a computer-assisted with a friendly graphical interface allows a non-expert user obtain and quickly visualize morphological and thermal parameters of the footprint plantar. Some diabetic and non-diabetic patients are analyzed and fusion of morphological and thermal information demonstrates to be a powerful tool for podological diagnosis.

The contour analysis and image-superimposition problem in computer vision systems

The possibility of using contour analysis was examined for solving the image superimposition problem in multispectral computer vision systems (images that are obtained from the sensors of this system and images that are synthesized on a digital locality map). Unique algorithms and modifications of the known algorithms that allow the solution of the image superimposition problem in the real-time mode are presented. Examples of the processing of real images in accordance with the proposed technology are given.

Citrus sorting by identification of the most common defects using multispectral computer vision

The presence of skin defects is one of the most influential factors in the price of fruit. The detection of defects during packing operations ensures that only fruits with a good quality reach the market. Moreover, the identification of the type of each defect will increase both the quality of the fruit and also the producer’s profit. At the present time, fruit with slight defects is marketed together with sound fruit, thus depreciating the quality of the batch, or it is removed together with seriously damaged fruit, thereby causing economic losses. Most current computer vision systems used in the automatic quality inspection of food are limited to the visible region of the electromagnetic spectrum as they tend to imitate the human eye. However, non-visible information, such as that provided by near-infrared or ultraviolet regions of the spectrum, can improve the inspection by detecting specific defects or allowing the detection of non-visible damages. This work summarises our research in the application of near-infrared, ultraviolet and fluorescence computer vision systems in the identification of the most common defects of citrus fruits, and proposes a fruit sorting algorithm that combines this different spectral information (including visible) to classify fruit according to the type of defect. Results showed that the contribution of non-visible information can improve the detection and identification of some defects. Compared with the results from colour images, the detection accuracy of anthracnose increased from 86% by using NIR images; and the accuracy of green mould was increased from 65% to 94% by using images of fluorescence.

An FPGA Based Coprocessor for GLCM and Haralick Texture Features and their Application in Prostate Cancer Classification

Grey Level Co-occurrence Matrix (GLCM), one of the best known tool for texture analysis, estimates image properties related to second-order statistics. These image properties commonly known as Haralick texture features can be used for image classification, image segmentation, and remote sensing applications. However, their computations are highly intensive especially for very large images such as medical ones. Therefore, methods to accelerate their computations are highly desired. This paper proposes the use of programmable hardware to accelerate the calculation of GLCM and Haralick texture features. Further, as an example of the speedup offered by programmable logic, a multispectral computer vision system for automatic diagnosis of prostatic cancer has been implemented. The performance is then compared against a microprocessor based solution.