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The segmentation of breast Magnetic Resonance Imaging (MRI) has been a long term challenge due to the fuzzy boundaries among objects, small spots, and irregular object shapes in breast MRI. Even though intensity-based clustering algorithms such as K-means clustering and Fuzzy C-means clustering have been used widely for basic image segmentation, they(More)
Magnetic resonance imaging (MRI) is one of the high quality technologies to detect the breast cancer. This study proposes a new framework to extract abnormal features in Magnetic Resonance (MR) images by concentrating on the key aspect of the features: generating a unique input sequence to apply the Support Vector Machine (SVM) classifier. The main(More)
Morphologic appearance plays a substantial role in presenting mass lesion in breast imaging. In this paper, we propose an innovative shape irregularity measurement based on roughness index - Enhance Roughness Index (ERI). This new irregularity measurement is taken as an input to Gaussian Mixture Model (GMM) classifier. By analyzing the similarity through(More)
About 1 in 8 women in the United States is expected to develop breast cancer over the course of herentire lifetime but a few medical imaging techniques have been applied for breast cancer screening. In addition, the feature extraction and comparison in medical images for breast cancer detection haverarely been reported in literature. We propose a new(More)
Morphologic appearance is one of intuitive diagnosis factors of mass lesions in breast imaging, and irregular shape is one of the most frequent appearances for malignant masses. Thus, an effective measure of morphological irregularity will provide a helpful reference to determine malignancy of breast masses. In this paper, a new measure based on Fourier(More)
In this paper, we propose a dynamic compressive spectrum sensing structure using adaptive compression sequences. The proposed scheme employs appropriate compression sequences depending on the partial information of input signal to enhance signal detection performance. Simulation results verify that the proposed compressive sensing method improves detection(More)
Metabolomic analysis of urinary polyamines (PAs) from rat exposed to 915 MHz radiofrequency identification (RFID) signal for 8 h/day for 2 weeks was performed by gas chromatography–mass spectrometry as N-ethoxycarbonyl/N-pentafluoropropionyl derivatives. Large alterations in nine PA levels including four aliphatic and five acetylated PAs were monitored in(More)
A performance of classification tool in a Computer Aided Diagnosis (CAD) software directly affects capacity of entire breast cancer screening. Most developed classification tools have mainly focused on standard techniques, for example, Magnetic Resonance Imaging (MRI), x-ray mammography, and ultrasound. With the advent of new technology, Microwave(More)
In this paper, we propose a compressive spectrum sensing method using sequences with low cross-correlation in frequency domain. Instead of satisfying conventional restricted isometry property, the proposed scheme tries to improve signal detection performance directly. Simulation results prove that the proposed method significantly improve system performance.
This paper proposes and evaluates the performance of a cooperative position fix scheme built on top of a group management mechanism carried out by processes distributed across the vehicular network. The main goal is to enhance the accuracy of map matching and thus the quality of diverse location-based services without extra equipment. Designed on the(More)