Diagnosis of prostatic Carcinoma on multiparametric magnetic resonance imaging using shearlet transform

Rezaeilouyeh, Hadi; Mahoor, Mohammad H.; Zhang, Jun Jason; Rosa, Francisco G. La; Chang, Sun O; Werahera, Priya N.

doi:10.1109/embc.2014.6945103

Cited by 6 publications

(11 citation statements)

References 24 publications

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“…[5][6][7][8] Wavelets do not have directional sensitivity, which makes them unsuitable for detecting directional features. That was the motive for using shearlets instead of wavelets in our previous studies [9][10][11][12] and in this paper as well. On the other hand, recent feature learning methods have gained a lot of attention due to the success of deep neural networks methods in computer vision applications.…”

Section: Introductionmentioning

confidence: 78%

“…23 One way to represent statistical properties of shearlet coefficients is to extract histograms from the magnitude of shearlet coefficients. We previously used HSCs for breast cancer detection 9 and prostate cancer detection 11 and Gleason grading. 10,12 Figure 4 shows the histogram of magnitude of shearlet coefficients for two cases.…”

Section: Features Extracted From Shearlet Transformmentioning

confidence: 99%

“…Some other techniques that used hand-crafted features are based on texture, color, and morphological features. [7][8][9][10][11][12] Jafari-Khouzani and Soltanian-Zadeh 7 extracted energy and entropy from multiwavelets coefficients and used them for the task of automatic Gleason grading of prostate. They used the k-nearest neighbor algorithm to classify images.…”

Section: Introductionmentioning

confidence: 99%

“…In an earlier study, 9 we extracted features from multidecomposition levels of shearlet filters and used the histogram of shearlet coefficients (HSCs) for the task of classification of benign and malignant breast slides using support vector machine (SVM) and achieved 75% classification accuracy. We also used HSC for prostate cancer detection of histological images 10 and magnetic resonance (MR) images 11 and achieved 100% and 97% classification accuracy, respectively. In a recent study, 12 we extracted multiple features from the histological images of prostate cancer and used the multiple kernel learning (MKL) algorithm for fusing the features.…”

Section: Introductionmentioning

confidence: 99%

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Microscopic medical image classification framework via deep learning and shearlet transform

2016

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Abstract. Cancer is the second leading cause of death in US after cardiovascular disease. Image-based computer-aided diagnosis can assist physicians to efficiently diagnose cancers in early stages. Existing computeraided algorithms use hand-crafted features such as wavelet coefficients, co-occurrence matrix features, and recently, histogram of shearlet coefficients for classification of cancerous tissues and cells in images. These hand-crafted features often lack generalizability since every cancerous tissue and cell has a specific texture, structure, and shape. An alternative approach is to use convolutional neural networks (CNNs) to learn the most appropriate feature abstractions directly from the data and handle the limitations of hand-crafted features. A framework for breast cancer detection and prostate Gleason grading using CNN trained on images along with the magnitude and phase of shearlet coefficients is presented. Particularly, we apply shearlet transform on images and extract the magnitude and phase of shearlet coefficients. Then we feed shearlet features along with the original images to our CNN consisting of multiple layers of convolution, max pooling, and fully connected layers. Our experiments show that using the magnitude and phase of shearlet coefficients as extra information to the network can improve the accuracy of detection and generalize better compared to the state-of-the-art methods that rely on hand-crafted features. This study expands the application of deep neural networks into the field of medical image analysis, which is a difficult domain considering the limited medical data available for such analysis.

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Section: Introductionmentioning

confidence: 78%

Section: Features Extracted From Shearlet Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Microscopic medical image classification framework via deep learning and shearlet transform

2016

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“…Textural analysis is another technique which assesses the variability of signal within a lesion to obtain quantitative parameters which can help differentiate malignant tissue [26]. A recent abstract by Rezaeilouyeh et al describes an application of shearlet transformation, in effect, an enhanced edge detection algorithm, to obtain sensitivities of 92, 100, and 89 on T2W, DWI, and DCE images, respectively, in a small cohort [27]. Yet another approach is to use multiple computer-derived imaging parameters to define the characteristics of a tumor.…”

Section: Prostate Mrimentioning

confidence: 99%

Novel Imaging of Prostate Cancer with MRI, MRI/US, and PET

et al. 2015

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Imaging of prostate cancer presents many challenges to the imaging community. There has been much progress in this space in large part due to MRI and PET radiopharmaceuticals. Though MRI has been focused on the evaluation of local disease and PET on the detection of metastatic disease, these two areas do converge and will be complementary especially with the growth of new PET/MRI technologies. In this review article, we review novel MRI, MRI/US, and PET radiopharmaceuticals which will offer insight into the future direction of imaging in prostate cancer.

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Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

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Diagnosis of prostatic Carcinoma on multiparametric magnetic resonance imaging using shearlet transform

Cited by 6 publications

References 24 publications

Microscopic medical image classification framework via deep learning and shearlet transform

Microscopic medical image classification framework via deep learning and shearlet transform

Novel Imaging of Prostate Cancer with MRI, MRI/US, and PET

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

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