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Intelligent Computer Aided Diagnosis (CAD) Systems can be used for detecting Microcalcification (MC) clusters in digital mammograms at the early stage. CAD systems help radiologists in identifying tumor patterns in an efficient and faster manner than other detection methods. In this paper, we propose a new approach for detecting tumors in mammograms using Radial Basis Function Networks (RBFNN). Prior...
Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. The main...
Computer aided diagnosis plays an important role in automatic detection of abnormal shadow area on CT images. The method of an automatic detection of lung nodules regions of interest (ROI) is presented in this paper. Firstly, the lung areas are segmented from CT images. Then, the initial ROI including nodules and blood vessels in lung areas are extracted by Top-hat filter. Thirdly, the second ROI...
A pulmonary nodule is the most common sign of lung cancer. The proposed system efficiently predicts lung tumor from Computed Tomography (CT) images through image processing techniques coupled with neural network classification as either benign or malignant. The lung CT image is denoised using non-linear total variation algorithm to remove random noise prevalent in CT images. Optimal thresholding is...
Mammography is the most effective method for the early diagnosis and treatment of breast Cancer diseases. However, data sets collected by image sensors are generally contaminated by noise. This ensures the need for image enhancement to aid interpretation. This paper introduces an efficient enhancement algorithm of digital mammograms based on wavelet analysis and modified mathematical morphology. In...
To date, cancer of the uterine cervix is still a leading cause of cancer-related deaths in women in the world. Papanicolau smear test is a well-known screening method of detecting abnormalities in the uterine cervix cells. In Indonesia, Pap smear test is mostly still done conventionally. Due to the small number of skilled and experienced cytologists, the screening procedure becomes time consuming...
In this paper, we present an intelligent approach to analysing prostrate ultrasound images in order to diagnose prostate cancer. Algorithms based on fuzzy image processing are applied first to enhance the contrast of the original image, to extract the region of interest and to enhance the edges surrounding that region. Then, we extract features characterising the underlying texture of the regions...
Medical images like mammograms are very difficult to analyze because of their low contrast. Different fractal features are used for analyzing mammograms in this paper. The new fractal feature derived from the modified average image is found to be a better feature for distinguishing between normal, malignant, benign and mammograms with microcalcifications. The study is performed on the mammograms obtained...
The applicability and reliability of Infrared (IR) spectroscopy to distinguish normal and abnormal cells has opened this research to obtain new features from IR spectral of cervical cells to be fed into multilayered perceptrons (MLP) networks. In order for neural networks to be used as cervical precancerous diagnostic system, the features of cervical cell were used as inputs for neural networks and...
In this study we present an integrated system for supporting the diagnosis of endometrial cancer. The system consists of an electronic patient record that incoporates a hysteroscopy imaging CAD system for the early detection of endometrial cancer. The electronic patient record is based on information collected from: appointments, patient info, hysteroscopy reporting and pharmacy. The CAD system is...
Strong evidence shows that characteristic patterns of breast tissues as seen on mammography, referred to as mammographic parenchymal patterns, provide crucial information about breast cancer risk. Quantitative evaluation of the characteristic mixture of breast tissues can be used as for mammographic risk assessment as well as for quantification of change of the relative proportion of different breast...
Colorectal cancer is the third most common cancer diagnosed in men and women. Generally surgery is by total excision of the mesorectum (TME), though it often has a poor outcome due to affected lymph nodes close to the resection boundary. Advancements in diagnosis and treatment of colorectal cancer require integration of information from different sources such as pathology macroscopic and microscopic...
Autofluorescence bronchoscopy (AFB) has been utilized over the past decade, proving to be a powerful tool for the detection and localization of premalignant and malignant lesions of the airways. AFB is, however, characterized by low specificity and a high rate of false positive findings (FPFs). The majority of FPFs are due to inflammations, as they often fluoresce at the same wavelengths with cancer...
The presence of microcalcifications clusters, which appear as small bright spots in mammographic images, can be considered as a very important sign for breast cancer diagnosis. They can, however, be hard to detect due to their size and low contrast from surrounding normal tissue. In this paper, a new fuzzy-based method is presented to provide an appropriate segmentation of microcalcifications. This...
2D wavelet transform decomposition is widely used in computer aided detection of microcalcifications in mammograms. The aim of this paper is to investigate the better type of wavelet and its optimal potential level of decomposition that gives us better detection. Our algorithm consists of four steps: first, dimension reduction is performed on the mammography images to delimitate the ROI (region of...
This paper proposes a novel method for breast cancer diagnosis using the feature generated by genetic programming (GP) based on Fisher criterion. GP as an evolutionary mechanism provides a training structure to generate features. Fisher criterion is employed to help GP optimize features whose values corresponding to pattern vector belonging to the same class are extremely similar while those corresponding...
Selecting the most possibly cancer-related genes from huge microarray gene expression data is an important bioinformatics research topic due to its significance to improve human's understandability of the inherent cancer-resulting mechanism. This is actually a feature selection problem. The huge number of genes makes it impossible to execute an exhaustive search. In this work, we propose a recursive...
This work aims at automated segmentation of major lesions observed in early stages of cervical cancer which is the second most common cancer among women worldwide. The purpose of segmentation is to automatically determine the location for a biopsy to be taken for diagnosis, a process that is currently done manually. The acetowhite region, a major indicator of abnormality in the cervix image, is first...
One of the leading diseases in women is breast cancer. The detection in an earlier stage is done by indicating the presence of microcalcification or mass. We develop two detection systems that can help a radiologist to detect microcalcifications and masses in mammograms. In particular, we utilize Mamdani inference system with four features, i.e., B-descriptor, D-descriptor, average intensity inside...
The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high (in the thousands) compared to the number of data samples (in the tens or low hundreds); that is, the data dimension is large compared to the number of data points (such data is said to be undersampled)...
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