Diagnosis at an early stage is clinically important for the cure of skin cancer. However, since some skin cancers have similar intuitive characteristics, and dermatologists rely on subjective experience to distinguish skin cancer types, the accuracy is often suboptimal. Recently, the introduction of computer methods in the medical field has better assisted physicians to improve the recognition rate but some challenges still exist. In the face of massive dermoscopic image data, residual network (ResNet) is more suitable for learning feature relationships inside big data because of its deeper network depth. Aiming at the deficiency of ResNet, this paper proposes a multi-region feature extraction and raising dimension matching method, which further improves the utilization rate of medical image features. This method firstly extracted rich and diverse features from multiple regions of the feature map, avoiding the deficiency of traditional residual modules repeatedly extracting features in a few fixed regions. Then, the fused features are strengthened by up-dimensioning the branch path information and stacking it with the main path, which solves the problem that the information of two paths is not ideal after fusion due to different dimensionality. The proposed method is experimented on the International Skin Imaging Collaboration (ISIC) Archive dataset, which contains more than 40,000 images. The results of this work on this dataset and other datasets are evaluated to be improved over networks containing traditional residual modules and some popular networks.

Breast cancer is a serious disease across the world, and it is one of the largest causes of cancer death for women. The traditional diagnosis is not only time consuming but also easily affected. Hence, artificial intelligence (AI), especially neural networks, has been widely used to assist to detect cancer. However, in recent years, the computational ability of a neuron has attracted more and more attention. The main computational capacity of a neuron is located in the dendrites. In this paper, a novel neuron model with dendritic nonlinearity (NMDN) is proposed to classify breast cancer in the Wisconsin Breast Cancer Database (WBCD). In NMDN, the dendrites possess nonlinearity when realizing the excitatory synapses, inhibitory synapses, constant-1 synapses and constant-0 synapses instead of being simply weighted. Furthermore, the nonlinear interaction among the synapses on a dendrite is defined as a product of the synaptic inputs. The soma adds all of the products of the branches to produce an output. A back-propagation-based learning algorithm is introduced to train the NMDN. The performance of the NMDN is compared with classic back propagation neural networks (BPNNs). Simulation results indicate that NMDN possesses superior capability in terms of the accuracy, convergence rate, stability and area under the ROC curve (AUC). Moreover, regarding ROC, for continuum values, the existing 0-connections branches after evolving can be eliminated from the dendrite morphology to release computational load, but with no influence on the performance of classification. The results disclose that the computational ability of the neuron has been undervalued, and the proposed NMDN can be an interesting choice for medical researchers in further research.

A microwave mammography setup for clinical testing was developed and used to successfully carry out an initial clinical test. The equipment is based on multistatic ultra wideband (UWB) radar, which features a multistatic microwave imaging via space time (MS-MIST) algorithm for high resolution and a conformal array with an aspirator for fixing the breast in place. In this paper, an outline of the equipment, a numerical simulation, and clinical test results are presented.

Histopathology is a microscopic anatomical study of body tissues and widely used as a cancer diagnosing method. Generally, pathologists examine the structural deviation of cellular and sub-cellular components to diagnose the malignancy of body tissues. These judgments may often subjective to pathologists' skills and personal experiences. However, computational diagnosis tools may circumvent these limitations and improve the reliability of the diagnosis decisions. This paper proposes a prostate image classification method by extracting textural behavior using multifractal analysis. Fractal geometry is used to describe the complexity of self-similar structures as a non-integer exponent called fractal dimension. Natural complex structures (or images) are not self-similar, thus a single exponent (the fractal dimension) may not be adequate to describe the complexity of such structures. Multifractal analysis technique has been introduced to describe the complexity as a spectrum of fractal dimensions. Based on multifractal computation of digital imaging, we obtain two textural feature descriptors; i) local irregularity: α and ii) global regularity: f(α). We exploit these multifractal feature descriptors with a texton dictionary based classification model to discriminate cancer/non-cancer tissues of histopathology images of H&E stained prostate biopsy specimens. Moreover, we examine other three feature descriptors; Gabor filter bank, LM filter bank and Haralick features to benchmark the performance of the proposed method. Experiment results indicated that the performance of the proposed multifractal feature descriptor outperforms the other feature descriptors by achieving over 94% of correct classification accuracy.

One kind of functional noncoding RNAs, microRNAs (miRNAs), form a class of endogenous RNAs that can have important regulatory roles in animals and plants by targeting transcripts for cleavage or translation repression. Researches on both experimental and computational approaches have shown that miRNAs indeed involve in the human cancer development and progression. However, the miRNAs that contribute more information to the distinction between the normal and tumor samples (tissues) are still undetermined. Recently, the high-throughput microarray technology was used as a powerful technique to measure the expression level of miRNAs in cells. Analyzing this expression data can allow us to determine the functional roles of miRNAs in the living cells. In this paper, we present a computational method to (1) predicting the tumor tissues using high-throughput miRNA expression profiles; (2) finding the informative miRNAs that show strong distinction of expression level in tumor tissues. To this end, we perform a support vector machine (SVM) based method to deeply examine one recent miRNA expression dataset. The experimental results show that SVM-based method outperforms other supervised learning methods such as decision trees, Bayesian networks, and backpropagation neural networks. Furthermore, by using the miRNA-target information and Gene Ontology annotations, we showed that the informative miRNAs have strong evidences related to some types of human cancer including breast, lung, and colon cancer.

An experimental study concerning Ultra Wideband (UWB) Microwave Radar for breast cancer detection is described. A simple phantom, consisting of a cylindrical plastic container with a low dielectric constant material imitating fatty tissues and a high dielectric constant object emulating tumour, is scanned with a tapered slot antenna operating between 3.1 to 10.6 GHz. A successful detection of a target is accomplished by a visual inspection of a two-dimensional image of the scanned phantom

Automatic detection of normal mammograms, as a "first look" for breast cancer, is a new approach to computer-aided diagnosis. This approach may be limited, however, by two main causes. The first problem is the presence of poorly separable "crossed-distributions" in which the correct classification depends upon the value of each feature. The second problem is overlap of the feature distributions that are extracted from digitized mammograms of normal and abnormal patients. Here we introduce a new Support Vector Machine (SVM) based method utilizing with the proposed uncrossing mapping and Local Probability Difference (LPD). Crossed-distribution feature pairs are identified and mapped into a new features that can be separated by a zero-hyperplane of the new axis. The probability density functions of the features of normal and abnormal mammograms are then sampled and the local probability difference functions are estimated to enhance the features. From 1,000 ground-truth-known mammograms, 250 normal and 250 abnormal cases, including spiculated lesions, circumscribed masses or microcalcifications, are used for training a support vector machine. The classification results tested with another 250 normal and 250 abnormal sets show improved testing performances with 90% sensitivity and 89% specificity.

Gastric cancer is a great part of the cancer occurrence and the mortality from cancer in Korea, and the early detection of gastric cancer is very important in the treatment and convalescence. This paper, for the early detection of gastric cancer, proposes the analysis system of an endoscopic image of the stomach, which detects abnormal regions by using the change of color in the image and by providing the surface tissue information to the detector. While advanced inflammation or cancer may be easily detected, early inflammation or cancer is difficult to detect and requires more attention to be detected. This paper, at first, converts an endoscopic image to an image of the IHb (Index of Hemoglobin) model and removes noises incurred by illumination and, automatically detects the regions suspected as cancer and provides the related information to the detector, or provides the surface tissue information for the regions appointed by the detector. This paper does not intend to provide the final diagnosis of abnormal regions detected as gastric cancer, but it intends to provide a supplementary mean to reduce the load and mistaken diagnosis of the detector, by automatically detecting the abnormal regions not easily detected by the human eye and this provides additional information for diagnosis. The experiments using practical endoscopic images for performance evaluation showed that the proposed system is effective in the analysis of endoscopic images of the stomach.

In this paper, an automated computer-aided-detection scheme is proposed to identify and locate the suspicious masses in the abnormal breasts from the full mammograms. Mammograms are examined using a four-stage detection method including pre-processing, identification of local maxima, seeded region-growing, and false positive (FP) reduction. This method has been applied to the entire Mammographic Image Analysis Society (MIAS) database of 322 digitized mammograms containing 59 biopsy-proven masses in 56 images. Results of detection show 95% true positive (TP) fraction at 1.9 FPs per image for the 56 images and 1.3 FPs per image for the entire database.

CR (Computed Radiography) is characterized by high sensitivity and wide dynamic range. Moreover, it has the advantage of being able to transfer exposed images directly to a computer-aided detection (CAD) system which is not possible using conventional film digitizer systems. This paper proposes a high-performance clustered microcalcification detection system for CR mammography. Before detecting and classifying candidate regions, the system preprocesses images with a normalization step to take into account various imaging conditions and to enhance microcalcifications with weak contrast. Large-scale experiments using images taken under various imaging conditions at seven hospitals were performed. According to analysis of the experimental results, the proposed system displays high performance. In particular, at a true positive detection rate of 97.1%, the false positive clusters average is only 0.4 per image. The introduction of geometrical features of each microcalcification for identifying true microcalcifications contributed to the performance improvement. One of the aims of this study was to develop a system for practical use. The results indicate that the proposed system is promising.

In this paper, we propose a new computer-aided diagnosis system which can extract specific features from hematoxylin and eosin (HE)-stained breast tumor images and evaluate the type of tumor using artificial organisms. The gene of the artificial organisms is defined by three kinds of texture features, which can evaluate the specific features of the tumor region in the image. The artificial organisms move around in the image and investigate their environmental conditions during the searching process. When the target pixel is regarded as a tumor region, the organism obtains energy and produces offspring; organisms in other regions lose energy and die. The searching process is iterated until the 30th generation; as a result, tumor regions are filled with artificial organisms. Whether the detected tumor is benign or malignant is evaluated based on the combination of selected genes. The method developed was applied to 27 test cases and the distinction between benign and malignant tumors by the artificial organisms was successful in about 90% of tumor images. In this diagnosis support system, the combination of genes, which represents specific features of detected tumor region, is selected automatically for each tumor image during the searching process.

The use of the homomorphic filter technique is described in order to enhance the contrast in the mammographic images, which is adopted to the dyadic wavelet transform. The proposed method has employed the nonlinear enhancement in homomorphic filtering as well as denoising method in the wavelet domains. Experimental results show that the homomorphic filtering method improves the contrast in breast tumor images such that the contrast improvement index is increased by two fold compared to the conventional wavelet-based enhancement technique.

This paper presents a method for detection of calcification, which is an important early sign of breast cancer in mammograms. Since information of calcifications is located in inhomogeneous background and noises, it is hard to be detected. This method uses wavelet packet transform (WPT) for elimination of the background image related to low frequency components. However, very high frequency signals of noises exist with the calcifications and make it hard to suppress them. Since calcification location can be represented as vertical, horizontal, and diagonal edges in time-frequency domain, the edges in spatial domain can be utilized as a filter for noise suppression. Then the image from inverse transform will contain only required information. A free-response operating characteristic (FROC) curve is used to evaluate a performance of proposed method by applying it to thirty images of calcifications. The results show 82.19 percent true positive detection rate at the cost of 6.73 false positive per image.

This paper proposes an automatic system which can perform the entire diagnostic process from the extraction of the liver to the recognition of a tumor. In particular, the proposed technique uses shape information to identify and recognize a lesion adjacent to the border of the liver, which can otherwise be missed. Because such an area is concave like a bay, morphological operations can be used to find the bay. In addition, since the intensity of a lesion can vary greatly according to the patient and the slice taken, a decision on the threshold for extraction is not easy. Accordingly, the proposed method extracts the lesion by means of a Fuzzy c-Means clustering technique, which can determine the threshold regardless of a changing intensity. Furthermore, in order to decrease any erroneous diagnoses, the proposed system performs a 3-D consistency check based on three-dimensional information that a lesion mass cannot appear in a single slice independently. Based on experimental results, these processes produced a high recognition rate above 91%.

This paper describes a method for classification of hematoxylin and eosin (HE)-stained breast tumor images into benign or malignant using the adaptive searching ability of artificial organisms. Each artificial organism has some attributes, such as, age, internal energy and coordinates. In addition, the artificial organism has a differentiation function for evaluating "malignant" or "benign" tumors and the adaptive behaviors of each artificial organism are evaluated using five kinds of texture features. The texture feature of nuclei regions in normal mammary glands and that of carcinoma regions in malignant tumors are treated as "self" and "non-self," respectively. This model consists of two stages of operations for detecting tumor regions, the learning and searching stages. At the learning stage, the nuclei regions are roughly detected and classified into benign or malignant tumors. At the searching stage, the similarity of each organism's environment is investigated before and after the movement for detecting breast tumor regions precisely. The method developed was applied to 21 cases of test images and the distinction between malignant and benign tumors by the artificial organisms was successful in all cases. The proposed method has the following advantages: the texture feature values for the evaluation of tumor regions at the searching stage are decided automatically during the learning stage in every input image. Evaluation of the environment, whether the target pixel is a malignant tumor or not, is performed based on the angular difference in each texture feature. Therefore, this model can successfully detect tumor regions and classify the type of tumors correctly without affecting a wide variety of breast tumor images, which depends on the tissue condition and the degree of malignancy in each breast tumor case.

In this paper, we present a computer-aided diagnosis (CAD) system to automatically detect lung cancer candidates at an early stage using a present and a past helical CT screening. We have developed a slice matching algorithm that can automatically match the slice images of a past CT scan to those of a present CT scan in order to detect changes in the lung fields over time. The slice matching algorithm consists of two main process: the process of extraction of the lungs, heart, and descending aorta and the process of matching slices of the present and past CT images using the information of the lungs, heart, and descending aorta. To evaluate the performance of this algorithm, we applied it to 50 subjects (total of 150 scans) screened between 1993 and 1998. From these scans, we selected 100 pairs for evaluation (each pair consisted of scans for the same subject). The algorithm correctly matched 88 out of the 100 pairs. The slice images for the present and past CT scans are displayed in parallel on the CRT monitor. Feature measurements of the suspicious regions are shown on the relevant images to facilitate identification of changes in size, shape, and intensity. The experimental results indicate that the CAD system can be effectively used in clinical practice to increase the speed and accuracy of routine diagnosis.

In our current work, we attempt to make an automatic diagnostic system of lung cancer based on the analysis of the sputum color images. In order to form general diagnostic rules, we have collected a database with thousands of sputum color images from normal and abnormal subjects. As a first step, in this paper, we present a segmentation method of sputum color images prepared by the Papanicalaou standard staining method. The segmentation is performed based on an energy function minimization using an unsupervised Hopfield neural network (HNN). This HNN have been used for the segmentation of magnetic resonance images (MRI). The results have been acceptable, however the method have some limitations due to the stuck of the network in an early local minimum because the energy landscape in general has more than one local minimum due to the nonconvex nature of the energy surface. To overcome this problem, we have suggested in our previous work some contributions. Similarly to the MRI images, the color images can be considered as multidimensional data as each pixel is represented by its three components in the RGB image planes. To the input of HNN we have applied the RGB components of several sputum images. However, the extreme variations in the gray-levels of the images and the relative contrast among nuclei due to unavoidable staining variations among individual cells, the cytoplasm folds and the debris cells, make the segmentation less accurate and impossible its automatization as the number of regions is difficult to be estimated in advance. On the other hand, the most important objective in processing cell clusters is the detection and accurate segmentation of the nuclei, because most quantitative procedures are based on measurements of nuclear features. For this reason, based on our collected database of sputum color images, we found an algorithm for NonSputum cell masking. Once these masked images are determined, they are given, with some of the RGB components of the raw image, to the input of HNN to make a crisp segmentation by assigning each pixel to label such as Background, Cytoplasm, and Nucleus. The proposed technique has yielded correct segmentation of complex scene of sputum prepared by ordinary manual staining method in most of the tested images selected from our database containing thousands of sputum color images.

In the early stage of hyperthermia, a large number of engineering efforts have been done in the development or the improvement of the heating and temperature measuring techniques. However, they were not always satisfactory clinically. Thus, even in this moment, various engineering researches as well as the electromagnetic techniques for hyperthermia should be build up rapidly. This paper describes some of the highlights of developed or ongoing electromagnetic heating techniques in hyperthermia and identities a trend of emerging electromagnetic heating. Furthermore, the author emphasizes that few medical engineering efforts have been done in the boundary field between pure physics and clinics, and the proper way to develop the hyperthermia equipment is the best use of successes in the three essential regions: Physics, Biology and Clinics.