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Detection of pulmonary nodules has played a significant role in lung cancer diagnosis because nodules are the first suspicious symptoms for the likelihood of cancer. Margin characteristics of the pulmonary nodules provide essential radiological features to determine the possibility of malignancy. In general, benign nodules hold quite smooth margins whilst malignant ones hold irregular margins. The...
Recent advances in using quantitative ultrasound (QUS) methods have provided a promising framework to non-invasively and inexpensively monitor or predict the effectiveness of therapeutic cancer responses. One of the earliest steps in using QUS methods is contouring a region of interest (ROI) inside the tumour in ultrasound B-mode images. While manual segmentation is a very time-consuming and tedious...
Lung cancer is the deadliest type of cancer in the world, for both men and women. Hence early detection is the most promising way to improve the patient's chance for survival from lung cancer. The most common technique used to examine the lung cancer is Posterior and Anterior chest radiography and computerized tomography scans. PA chest radiography is the cost effective tool in diagnosing lung tumors...
Circulating tumor cells (CTCs) is an informative biomarker which assists pathologists in early diagnosis and evaluating therapeutic effects of patients with malignant tumors. The blood from a cancer patient is analyzed by a microscope and a large number of pictures including many cells are generated for each case. Thus, analyzing them is time-consuming work for pathologists, and misdiagnosis may happen...
Lung cancer is one among the major causes of cancer related deaths. Fortunately, an early stage diagnosis can increase the survival rates of the patients. Sputum cytology is one of the easiest and cost-effective method for lung cancer diagnosis. Chances of misdiagnosis and sampling error related to sputum cytology led to the concept of malignancy associated changes. Malignancy associated changes (MAC)...
There is currently a large amount of histopathological images due to the intensive prevention screening programs worldwide. This fact overloads the pathologists' tasks. Hence, there is a connected high need for a quantitative image-based evaluation of digital pathology slides. The current work extracts 76 numerical features from 357 histopathological images and focuses on the selection of the most...
Decisions about cervical cancer diagnosis and classification currently require microscopic examination of cervical tissue by an expert pathologist. In the present study, which focused on full automation of this approach, we solely use nucleus-level features to classify tissues as normal or cancer. We propose Adaptive Nucleus Shape Modeling (ANSM) algorithm for nucleus-level analysis which consists...
Lung cancer is the leading cause of cancer mortality around the world, the early diagnosis of lung cancer plays a very important role in therapeutic regimen selection. However, lung cancers are spatially and temporally heterogeneous; this limits the use of invasive biopsy. But radiomics which refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative...
This paper proposed an automated system for grading of colorectal cancer using image processing method. Almost, half a million people die every year due to colon cancer. Histopathological tissue analysis is a common method for its detection, which needs an expert pathologist. Screening for this cancer is effective for prevention as well as early detection. The method proposed segment the glands automatically...
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...
Microscopic pictures are reviewed visually by hematologists and the procedure is tedious and time taking which causes late detection. Therefore automatic image handling framework is required that can overcome related limitations in visual investigation which provide early detection of disease and also type of cancer. The proposed strategy is effectively connected to many numbers of pictures, demonstrating...
Health informatics has been qualified as prominent province in the headway of information technology. Ascribable to such a sophisticated evolution in the health care informatics, it is viable at the present period of time to diagnose several ailments in a short span of time. In relation to complaints, there is one disease dub leukemia which can be recognised by manipulating different techniques of...
In this paper, we propose a new framework, namely hybrid classifiers ensemble with random undersampling for liver tumor segmentation. Essentially, the proposed framework is working on computed tomography images in which each pixel is represented by a rich feature vector. To handle the class imbalance problem, those pixels which correspond to non-tumor region are randomly subsampled. Outcomes of three...
Based on previous work on regional temporal mammogram registration, this study investigates the combination of image features measured from single regions (single features) and image features measured from the matched regions of temporal mammograms (temporal features) for the classification of malignant masses. Three SVM kernels, the multilayer perceptron kernel, the polynomial kernel, and the gaussian...
In this paper, we propose a method for classification of histopathological images using texture features. The images are first segmented as epithelial lining surrounding the lumen for breast histopathology images using spatio-color-texture graph segmentation method. The features such as Gray Level Co-occurrence Matrix (GLCM), Graph Run Length Matrix (GRLM) features, and Euler number are extracted...
The objective of this paper is to classify likely cancerous and noncancerous lung image and to detect the location of the nodule in the lung image provided by CT scan. The novelness of this paper is to provide better accuracy and assists radiologist to analyze CT scan images of lung accurately. This efficient proposed method consists of image enhancement, extracting region of interest using Active...
Clinical Decision Support (CDS) aids in early diagnosis of liver cancer, a potentially fatal disease prevalent in both developed and developing countries. Our research aims to develop a robust and intelligent clinical decision support framework for disease management of cancer based on legacy Ultrasound (US) image data collected during various stages of liver cancer. The proposed intelligent CDS framework...
Contrast enhancement is one of the important steps in image processing. Enhancement process has a vital role in medical image processing. Histogram Equalization (HE) plays the major role in enhancement process. HE is simple and effective method in contrast enhancement. The conventional HE enhancement process outputted an excessive contrast result. Which leads to poor classification result, especially...
Diagnosis of lung cancer in its primal stage is a major problem faced by the medical world. For that proper details are needed from the images, which can only be obtained by a good segmentation method. However, many common forms of techniques are available in market and their major drawback is the accuracy of segmentation of the nucleus from the ROI and also the time consumed for the same. Since this...
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