Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions

Breast cancer is the most frequently diagnosed malignancy and the second leading cause of mortality in women. In the last decade, ultrasound along with digital mammography has come to be regarded as the gold standard for breast cancer diagnosis. Automatically detecting tumors and extracting lesion boundaries in ultrasound images is difficult due to their specular nature and the variance in shape and appearance of sonographic lesions. Past work on automated ultrasonic breast lesion segmentation has not addressed important issues such as shadowing artifacts or dealing with similar tumor like structures in the sonogram. Algorithms that claim to automatically classify ultrasonic breast lesions, rely on manual delineation of the tumor boundaries. In this paper, we present a novel technique to automatically find lesion margins in ultrasound images, by combining intensity and texture with empirical domain specific knowledge along with directional gradient and a deformable shape-based model. The images are first filtered to remove speckle noise and then contrast enhanced to emphasize the tumor regions. For the first time, a mathematical formulation of the empirical rules used by radiologists in detecting ultrasonic breast lesions, popularly known as the "Stavros Criteria" is presented in this paper. We have applied this formulation to automatically determine a seed point within the image. Probabilistic classification of image pixels based on intensity and texture is followed by region growing using the automatically determined seed point to obtain an initial segmentation of the lesion. Boundary points are found on the directional gradient of the image. Outliers are removed by a process of recursive refinement. These boundary points are then supplied as an initial estimate to a deformable model. Incorporating empirical domain specific knowledge along with low and high-level knowledge makes it possible to avoid shadowing artifacts and lowers the chance of confusing similar tumor like structures for the lesion. The system was validated on a database of breast sonograms for 42 patients. The average mean boundary error between manual and automated segmentation was 6.6 pixels and the normalized true positive area overlap was 75.1%. The algorithm was found to be robust to 1) variations in system parameters, 2) number of training samples used, and 3) the position of the seed point within the tumor. Running time for segmenting a single sonogram was 18 s on a 1.8-GHz Pentium machine.     

Segmenting skin lesions with partial-differential-equations-based image processing algorithms

In this paper, a partial-differential equations (PDE)-based system for detecting the boundary of skin lesions in digital clinical skin images is presented. The image is first preprocessed via contrast-enhancement and anisotropic diffusion. If the lesion is covered by hairs, a PDE-based continuous morphological filter that removes them is used as an additional preprocessing step. Following these steps, the skin lesion is segmented either by the geodesic active contours model or the geodesic edge tracing approach. These techniques are based on computing, again via PDEs, a geodesic curve in a space defined by the image content. Examples showing the performance of the algorithm are given.     

Overview of Advanced Computer Vision Systems for Skin Lesions Characterization

During the last years, computer-vision-based diagnosis systems have been used in several hospitals and dermatology clinics, aiming mostly at the early detection of skin cancer, and more specifically, the recognition of malignant melanoma tumour. In this paper, we review the state of the art in such systems by first presenting the installation, the visual features used for skin lesion classification, and the methods for defining them. Then, we describe how to extract these features through digital image processing methods, i.e., segmentation, border detection, and color and texture processing, and we present the most prominent techniques for skin lesion classification. The paper reports the statistics and the results of the most important implementations that exist in the literature, while it compares the performance of several classifiers on the specific skin lesion diagnostic problem and discusses the corresponding findings.     

A novel optimized neutrosophic <Emphasis Type="Italic">k</Emphasis>-means using genetic algorithm for skin lesion detection in dermoscopy images

This paper implemented a new skin lesion detection method based on the genetic algorithm (GA) for optimizing the neutrosophic set (NS) operation to reduce the indeterminacy on the dermoscopy images. Then, k-means clustering is applied to segment the skin lesion regions. Therefore, the proposed method is called optimized neutrosophic k-means (ONKM). On the training images set, an initial value of \(\alpha \) in the \(\alpha \)-mean operation of the NS is used with the GA to determine the optimized \(\alpha \) value. The Jaccard index is used as the fitness function during the optimization process. The GA found the optimal \(\alpha \) in the \(\alpha \)-mean operation as \(\alpha _{\mathrm{optimal}} =0.0014\) in the NS, which achieved the best performance using five fold cross-validation. Afterward, the dermoscopy images are transformed into the neutrosophic domain via three memberships, namely true, indeterminate, and false, using \(\alpha _{\mathrm{optimal}}\). The proposed ONKM method is carried out to segment the dermoscopy images. Different random subsets of 50 images from the ISIC 2016 challenge dataset are used from the training dataset during the fivefold cross-validation to train the proposed system and determine \(\alpha _{\mathrm{optimal}}\). Several evaluation metrics, namely the Dice coefficient, specificity, sensitivity, and accuracy, are measured for performance evaluation of the test images using the proposed ONKM method with \(\alpha _{\mathrm{optimal}} =0.0014\) compared to the k-means, and the \(\gamma \)–k-means methods. The results depicted the dominance of the ONKM method with \(99.29\pm 1.61\%\) average accuracy compared with k-means and \(\gamma \)–k-means methods.     

Medical Big Data Analysis with Attention and Large Margin Loss Model for Skin Lesion Application

Due to melanoma is one of the skin cancers with the highest mortality rate and have a large amount of data during the collection and diagnosis, there is an urgent need to improve the diagnostic efficiency and accuracy. However, there remain problems in analyzing medical big data for skin lesion application, such as the intra-class variation and inter-class similarity in skin lesion images and the lacks of ability to focus on the lesion area affecting the classification results of the model. To address these dilemmas, in this paper, we proposed a novel machine learning-based approach that builds on top of DenseNet. It combines the attention mechanism and large margin loss to enhance the classification accuracy in terms of intra-class compactness and inter-class separability. We evaluated our model on ISIC 2017 (International Skin Imaging Collaboration) dataset, which has achieved 92% of Mean AUC. The experimental results show the effectiveness of our solution outperforms the state-of-the-art significantly in classify skin lesion and can accurately classify malignant melanoma on medical images.

     

Detection of basal cell carcinoma using electrical impedance and neural networks

Variations in electrical impedance over frequency might be used to distinguish basal cell carcinoma (BCC) from benign skin lesions, although the patterns that separate the two are nonobvious. Artificial neural networks (ANNs) may be good pattern classifiers for this application. A preliminary study to show the potential of neural networks to distinguish benign from malignant skin lesions using electrical impedance is presented. Electrical impedance was measured in vivo from 1 kHz to 1 MHz at five virtual depths on 18 BCC and 16 benign or premalignant lesions. A feed-forward neural network was trained using back propagation to classify these lesions. Two methods of preprocessing were used to account for the impedance of normal skin and the size of the lesion, one based on estimating the impedance of the lesion relative to adjacent normal skin and one based on estimating the impedance of the lesion independent of size or surrounding normal skin. Neural networks were able to classify measurements in a test set with 100% accuracy for the first preprocessing technique and 85% accuracy for the second. These results indicate electrical impedance may be a promising clinical diagnostic tool for basal cell carcinoma or other forms of skin cancer.     

Monte Carlo simulation of light-tissue interaction: three-dimensional simulation for trans-illumination-based imaging of skin lesions

Three-dimensional, voxel-based, and wavelength-dependent skin lesion models are developed and simulated using Monte Carlo techniques. The optical geometry of the Nevoscope with trans-illumination is used in the simulations for characterizing the lesion thickness. Based on the correlation analysis between the lesion thickness and the diffuse reflectance, optical wavelengths are selected for multispectral imaging of skin lesions using the Nevoscope. Tissue optical properties reported by various researchers are compiled together to form a voxel library. Tissue models used in the simulations are developed using the voxel library which offers flexibility in updating the optical properties and adding new media types into the models independent of the Monte Carlo simulation code.     

激光在皮肤病学应用中的最新进展

本文总结了近年来激光在皮肤病学方面应用的最新进展,着重讨论了激光治疗中的皮肤冷却技术以及激光在治疗血管性病变和色素性病变方面的发展。     

Transillumination imaging for blood oxygen saturation estimation of skin lesions

Detecting the early stages of melanoma can be greatly assisted by an accurate estimate of subsurface blood volume and blood oxygen saturation, indicative of angiogenesis. Visualization of this blood volume present beneath a skin lesion can be achieved through the transillumination of the skin. As the absorption of major chromophores in the skin is wavelength dependent, multispectral imaging can provide the needed information to separate out relative amounts of each chromophore. However, a critical challenge to this strategy is relating the pixel intensities observed in a given image to the wavelength-dependent total absorption existing at each spatial location. Consequently, in this paper, we develop an extension to Beer's law, estimated through a novel voxel-based, parallel processing Monte Carlo simulation of light propagation in skin which takes into account the specific geometry of our transillumination imaging apparatus. We then use this relation in a linear mixing model, solved using a multispectral image set, for chromophore separation and oxygen saturation estimation of an absorbing object located at a given depth within the medium. Validation is performed through the Monte Carlo simulation, as well as by imaging on a skin phantom. Results show that subsurface oxygen saturation can be reasonably estimated with good implications for the reconstruction of 3-D skin lesion volumes using transillumination toward early detection of malignancy.     

Detection of skin cancer by classification of Raman spectra

Skin lesion classification based on in vitro Raman spectroscopy is approached using a nonlinear neural network classifier. The classification framework is probabilistic and highly automated. The framework includes a feature extraction for Raman spectra and a fully adaptive and robust feedforward neural network classifier. Moreover, classification rules learned by the neural network may be extracted and evaluated for reproducibility, making it possible to explain the class assignment. The classification performance for the present data set, involving 222 cases and five lesion types, was 80.5%+/-5.3% correct classification of malignant melanoma, which is similar to that of trained dermatologists based on visual inspection. The skin cancer basal cell carcinoma has a classification rate of 95.8%+/-2.7%, which is excellent. The overall classification rate of skin lesions is 94.8%+/-3.0%. Spectral regions, which are important for network classification, are demonstrated to reproduce. Small distinctive bands in the spectrum, corresponding to specific lipids and proteins, are shown to hold the discriminating information which the network uses to diagnose skin lesions.     

He－Ne激光治疗放射性皮肤损伤的探讨（附45例分析）

应用Ｈｅ－Ｎｅ激光治疗放射性皮肤损伤４５例。Ｈｅ－Ｎｅ激光具有促进局部组织血液循环和新陈代谢，抗炎消肿，促进创面愈合的作用，可做为放射性皮肤损伤的一种辅助治疗手段。     

图像梯度场双区间均衡化的细节增强

直方图均衡化技术已广泛地应用于图像增强中,本文将该方法引入梯度域,这样可以使图像细节均匀地分布在各灰度级上.进一步本文又研究了图像梯度幅值分布特点,根据梯度幅值的右偏分布特性,采取双区间分别对梯度场进行均衡化处理.在区间阈值选取上,本文结合频率分布的数字特征,提出了区间阈值的两个选取准则并将梯度幅值合理地分为小梯度区间和大梯度区间.在目标梯度场复原过程中,本文提出矩阵变换法替代传统的"微分迭代法",减小了算法的时间复杂度.实验结果表明本文算法可使图像细节得到有效地增强.     

Integrated Behavior of Artificial Skin

This paper deals with a system of planary distributed transducers called "Artificial skin." The material used for this purpose is soft, homogeneous and elastic. The Belgrade hand prosthesis is covered with it. The aim of this paper is to find those properties of the sensor unit which would enable the optimal integrated behavior of the whole system to be obtained. Two basic integral properties of artificial skin are implemented: tactile perception and perception proportional to pressure on the skin. From this it is possible to have slipping perception of the grasped objects.     

Human‐Palm‐Inspired Artificial Skin Material Enhances Operational Functionality of Hand Manipulation

Human skin plays an important role in hand manipulation by making a stable grasp with an enlarging contact area while providing a firm hold on the object. However, satisfying these two functions is contradictory in conventional single‐layer artificial skin. Softer skin material would increase the contact area, which is advantageous in maintaining the stability, but it decreases the manipulability since the object tends to make uncontrollable movement within the softer skin, and vice versa for harder skin material. This paper presents a biomimetic three‐layer skin structure inspired by human palm skin and shows that both stability and manipulability can be enhanced with the three‐layer structure. To achieve the unique stiffness characteristics of the human palm skin, a porous latex structure, which is highly compressible but tough in tensile direction, is chosen as the subcutaneous fat layer. Through the novel experimental setup and the finite element method simulations, it is found that the porous latex structure is the key structure contributing to both stability and manipulability. Furthermore, it is demonstrated that a robotic hand with the proposed skin material shows enhanced robustness in grasping tasks. With the proposed skin material, the robotic hands would be more advantageous for challenging manipulation tasks.     

Theory of Thermal Fatigue Caused by RF Pulsed Heating

The normal conducting electron-positron Linear Collider projects imply that accelerating structures and other RF components will undergo an action of extremely high RF fields. Except for the RF breakdown threat, there is an effect of the copper surface being damage due to multi-pulse mechanical stress caused by Ohmic losses in the skin layer.In this paper we would like to introduce a new “grain” model of the processes responsible for the fatigue of the metal surface. This model is based on the quasi-elastic interaction between neighboring grains in the metal due to the thermal expansion of the skin layer. This mechanism of fatigue is compared with another, where stresses are generated by the temperature gradient towards the bulk of the material. With the proposed formalism one can estimate the total number of the RF pulses required to fracture the surface depending on the temperature rise, pulse length and steady state temperature. The parameters necessary to finalize the proposed approach were found through the comparison of experimental data obtained at 11.424 GHz.     

The thermoelectric power of a semiconductor <Emphasis Type="Italic">p-n</Emphasis> heterojunction

Extended Ge-GaAs p-n heterojunctions were fabricated and studied. The thermoelectric power of the heterojunction was measured as a function of the temperature difference at the structure ends, which was varied from 10 to 180 K, while the average temperature was maintained constant at 300 K. It is found that experimental data obtained at a large temperature gradient are consistent with theoretical predictions that take into account the emergence of minority charge carriers due to thermal emission.     

Nanoparticle‐Based Antivirulence Vaccine for the Management of Methicillin‐Resistant Staphylococcus aureus Skin Infection

With the rising threat of antibiotic‐resistant bacteria, vaccination is becoming an increasingly important strategy to prevent and manage bacterial infections. Made from deactivated bacterial toxins, toxoid vaccines are widely used in the clinic as they help to combat the virulence mechanisms employed by different pathogens. Here, the efficacy of a biomimetic nanoparticle‐based antivirulence vaccine is examined in a mouse model of methicillin‐resistant Staphylococcus aureus (MRSA) skin infection. Vaccination with nanoparticle‐detained staphylococcal α‐hemolysin (Hla) effectively triggers the formation of germinal centers and induces high anti‐Hla titers. Compared to mice vaccinated with control samples, those vaccinated with the nanoparticle toxoid show superior protective immunity against MRSA skin infection. The vaccination not only inhibits lesion formation at the site of bacterial challenge but also reduces the invasiveness of MRSA, preventing dissemination into other organs. Overall, this biomimetic nanoparticle‐based toxin detainment strategy is a promising method for the design of potent antivirulence vaccines for managing bacterial infections.     

The Expansions of Electromagnetic Fields in Cavities

In the theory of cavity resonators, the assumptions are frequently made that every irrotational function can be represented as the gradient of a scalar and that every divergenceless function can be represented as the rotation of a vector. These are, however, not necessarily correct. This paper corrects these misleading assumptions and describes "the theory of cavity resonators" which supplement the classical theory of Slater.