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This paper aims to classify a peripheral pulmonary lesion whether it is malignant or benign by proposing the new method to select a window of interest (WOI) using window slicing and the new feature called the "weight-sum of upper and lower gray level co-occurrence matrix (GLCM)" of an endobronchial ultrasound (EBUS) image. The proposed feature can be used to determine the heterogeneity of...
The paper presents the results of research on the use of Deep Neural Networks (DNN) for automatic classification of the skin lesions. The authors have focused on the most effective kind of DNNs for image processing, namely Convolutional Neural Networks (CNN). In particular, three kinds of CNN were analyzed: VGG19, Residual Networks (ResNet) and the hybrid of VGG19 CNN with the Support Vector Machine...
Early detection of lung cancer is of vital importance to successful treatment where Computed Tomography (CT) screening are considered one of the best methods for detection the early signs of lung cancer. Standard Computer Aided Diagnosis (CAD) systems for Lung cancer detection should employ four steps: preprocessing, lungs parenchyma segmentation, nodule detection and reduction of False Positives...
In this paper a method is proposed for segmenting glandular structures from Haematoxilyn and Eosin stained colon histopathology images. Gland includes three main structures: lumen, cytoplasm and cell nuclei. As the first step of the algorithm, k-means clustering algorithm is applied to cluster images to 3 clusters (lumen, cytoplasm and cell nuclei). Then, all lumen in each histopathology images are...
This paper presents a comparative study between Steerable Pyramid, Curvelet, Contourlet, and Gabor for breast cancer diagnosis in the full-field digital mammogram. Using multi-resolution and multi-orientation analysis, mammogram analysis images are decomposed into different resolution levels and orientations. A set of the biggest coefficients from each scale and orientation is extracted. Then a supervised...
This paper evaluated the performance of two-dimensional (2D) and 3D texture features from CT images on pulmonary nodules diagnosis using the large database LIDC-IDRI. Total of 905 nodules (422 malignant and 483 benign) with certain expert observer ratings of malignancy were extracted from the database based on the radiologists' painting boundaries. Feature analysis on the extracted nodules was not...
Breast Cancer is one of the frequent and leading causes of mortality among woman, especially in developed countries. Woman within the age of 40-69 have more risk of breast cancer. Though breast cancer leads to death, early detection of breast cancer can increase the survival rate. Clustered Microcalcification (MC) in mammograms is the major indication for early detection of breast cancer. MC is quiet...
Prostate cancer is considered to be one of the main causes of cancer related death for men in the United States. Automated methods for prostate cancer localization based on multispectral magnetic resonance imaging (MRI) haver recently emerged as a non invasive technique for this purpose as an alternative to transrectal ultrasound. However, the automated methods developed to this date require a manual...
Acute lymphoblastic leukemia (ALL) are a group of hematological neoplasia of childhood which is characterized by a large number of lymphoid blasts in the blood stream. ALL makes around 80% of childhood leukemia and it mostly occur in the age group of 3-7. The nonspecific nature of the signs and symptoms of ALL often leads to wrong diagnosis. Diagnostic confusion is also posed due to imitation of similar...
Acute lymphoblastic leukemia (ALL) is the most common hematological neoplasia of childhood and is characterized by uncontrolled growth of leukemic cells in bone marrow, lymphoid organs etc. The nonspecific nature of the signs and symptoms of ALL often leads to wrong diagnosis. Diagnostic confusion is also posed due to imitation of similar signs by other disorders. Careful microscopic examination of...
The characterization and quantitative description of histological images is not a simple problem. To reach a final diagnosis, usually the specialist relies on the analysis of characteristics easily observed, such as cells size, shape, staining and texture, but also depends on the hidden information of tissue localization, physiological and pathological mechanisms, clinical aspects, or other etiological...
With the rapid development of high-throughput genotyping technologies, more and more attentions are paid to the disease association study identifying DNA variations that are highly associated with a specific disease. One main challenge for this study is to find the optimal subsets of Single Nucleotide Polymorphisms (SNPs) which are most tightly associated with diseases. Feature selection which might...
To improve the accuracy and sensitivity of the breast tumor classification based on ultrasound images, a computer-aided classification algorithm is proposed using the Affinity Propagation (AP) clustering. Five morphologic features and three texture features are extracted from each breast ultrasound image. The AP clustering with an empirical value of "preference" is used as the primary classification...
This work focuses on the recognition of three-dimensional colon polyps captured by an active stereo vision sensor. The detection algorithm consists of SVM classifier trained on robust feature descriptors. The study is related to Cyclope, this prototype sensor allows real time 3D object reconstruction and continues to be optimized technically to improve its classification task by differentiation between...
Breast cancer is the most common cancer diagnosed among U.S. women. In this paper we have done some experiments for tumor detection in digital mammogram images. First of all, we have described a method that segments the breast image automatically. As a preprocessing, we have used fuzzy based noise removal filter that removes noise. Then for segmentation, we have provided a background removal method...
Respiratory gated radiotherapy for lung cancer allows for more precise delivery of prescribed radiation dose to the tumor, while minimizing normal tissue complications. Techniques for fluoroscopic gating without implanted fiducial markers have been developed in a classification framework. Due to the high-dimensionality nature of the images, dimensionality reduction techniques such as principal component...
In this paper, an algorithm for texture analysis of clustered calcification based on statistical texture models is proposed. The prior knowledge of both normal and lesion training samples are incorporated into statistical texture models separately. Specifically, beside texture analysis of the lesion tissues, and the resultant statistical parameters can also be used for unknown sample representation...
When solving the problem in computer assisted detection by the approach of pattern recognition, the lesion data always exhibited high-dimensional and inhomogeneous, which makes most of the traditional classifiers can not performance very well. In this paper, a novel approach based on the dynamic feature subset selection and the EM algorithm with Naive Bayesian classifier integration algorithm (DSFS+EMNB)...
This paper presents an automated system for grading pathological images of prostatic carcinoma based on a set of texture features extracted by multi-categories of methods including multi-wavelets, Gabor-filters, GLCM, and fractal dimensions. We apply 5-fold cross-validation procedure to a set of 205 pathological prostate images for training and testing. Experimental results show that the fractal dimension...
In this paper, we describe a two-stage hybrid approach to select gene features and produce dominant patterns for evaluating the pathological probability. To discover suitable genes as experiment samples for distinguishing the status of gene regulation, we utilized receiver operating characteristic (ROC) method to eliminate non-significant genes of unapparent variation between normal tissues and tumors...
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