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Image processing techniques for cancer classification

L Arokia Jesu Prabhu, A Jayachandran
Meningioma is the one of the most common type of brain tumor, it as arises from the meninges and encloses the spine and the brain inside the skull. It accounts for 30% of all types of brain tumor. Meningioma's can occur in many parts of the brain and accordingly it is named. In this paper, a mixture model based classification of meningioma brain tumor using MRI image is developed. The proposed method consists of four stages. In the first stage, with respect to the cells' boundary, it is necessary to further processing, which ensures the boundary of some cells is a discrete region...
November 3, 2018: Journal of Medical Systems
Nan Meng, Edmund Lam, Kevin Kin Man Tsia, Hayden Kwok-Hay So
Recent advances in ultra-high-throughput optical microscopy have enabled a new generation of cell classification methodologies using image-based cell phenotypes alone. In contrast to the current single-cell analysis techniques that rely solely on slow and costly genetic/epigenetic analyses, these image-based classification methods allow morphological profiling and screening of thousands or even millions of single cells at a fraction of the cost. Furthermore, they have demonstrated the statistical significance required for understanding the role of cell heterogeneity in diverse biological applications, ranging from cancer screening to drug candidate identification/validation processes...
October 31, 2018: IEEE Journal of Biomedical and Health Informatics
Nachiketh Soodana-Prakash, Radka Stoyanova, Abhishek Bhat, Maria C Velasquez, Omer E Kineish, Alan Pollack, Dipen J Parekh, Sanoj Punnen
Radiogenomics is a field that amalgamates data from genomics and imaging techniques in order to derive clinically meaningful trends. In this article, we discuss the importance of prostate cancer risk classification and how data derived from genomic testing and multi-parametric magnetic resonance imaging (mpMRI) can be integrated into clinical decision-making processes with a focus on active surveillance (AS). Finally, we describe an ongoing prospective trial (Miami MAST trial) which incorporates imaging (mpMRI) and radiomics data in patients who are on AS for prostate cancer...
September 2018: Translational Andrology and Urology
Aymen Mouelhi, Hana Rmili, Jaouher Ben Ali, Mounir Sayadi, Raoudha Doghri, Karima Mrad
BACKGROUND AND OBJECTIVE: This paper presents an improved scheme able to perform accurate segmentation and classification of cancer nuclei in immunohistochemical (IHC) breast tissue images in order to provide quantitative evaluation of estrogen or progesterone (ER/PR) receptor status that will assist pathologists in cancer diagnostic process. METHODS: The proposed segmentation method is based on adaptive local thresholding and an enhanced morphological procedure, which are applied to extract all stained nuclei regions and to split overlapping nuclei...
October 2018: Computer Methods and Programs in Biomedicine
Shenbagavalli P, Thangarajan R
Objective: Breast Cancer is the most invasive disease and fatal disease next to lung cancer in human. Early detection of breast cancer is accomplished by X-ray mammography. Mammography is the most effective and efficient technique used for detection of breast cancer in women and also to improve the breast cancer prognosis. The numbers of images need to be examined by the radiologists, the resulting may be misdiagnosis due to human errors by visual Fatigue. In order to avoid human errors, Computer Aided Diagnosis is implemented...
September 26, 2018: Asian Pacific Journal of Cancer Prevention: APJCP
Martin Halicek, James V Little, Xu Wang, Mihir Patel, Christopher C Griffith, Amy Y Chen, Baowei Fei
One of the largest factors affecting disease recurrence after surgical cancer resection is negative surgical margins. Hyperspectral imaging (HSI) is an optical imaging technique with potential to serve as a computer aided diagnostic tool for identifying cancer in gross ex-vivo specimens. We developed a tissue classifier using three distinct convolutional neural network (CNN) architectures on HSI data to investigate the ability to classify the cancer margins from ex-vivo human surgical specimens, collected from 20 patients undergoing surgical cancer resection as a preliminary validation group...
February 2018: Proceedings of SPIE
Mingquan Lin, Weifu Chen, Mingbo Zhao, Eli Gibson, Matthew Bastian-Jordan, Derek W Cool, Zahra Kassam, Huageng Liang, Tommy W S Chow, Aaron D Ward, Bernard Chiu
PURPOSE: Multiparametric MRI (mpMRI) has shown promise in the detection and localization of prostate cancer foci. Although techniques have been previously introduced to delineate lesions from mpMRI, these techniques were evaluated in datasets with T2 maps available. The generation of T2 map is not included in the clinical prostate mpMRI consensus guidelines; the acquisition of which requires repeated T2-weighted (T2W) scans and would significantly lengthen the scan time currently required for the clinically recommended acquisition protocol, which includes T2W, diffusion-weighted (DW), and dynamic contrast-enhanced (DCE) imaging...
October 2018: Medical Physics
Yue Du, Roy Zhang, Abolfazl Zargari, Theresa C Thai, Camille C Gunderson, Katherine M Moxley, Hong Liu, Bin Zheng, Yuchen Qiu
The tumor-stroma ratio (TSR) reflected on hematoxylin and eosin (H&E)-stained histological images is a potential prognostic factor for survival. Automatic image processing techniques that allow for high-throughput and precise discrimination of tumor epithelium and stroma are required to elevate the prognostic significance of the TSR. As a variant of deep learning techniques, transfer learning leverages nature-images features learned by deep convolutional neural networks (CNNs) to relieve the requirement of deep CNNs for immense sample size when handling biomedical classification problems...
July 26, 2018: Annals of Biomedical Engineering
Fei Yang, John C Ford, Nesrin Dogan, Kyle R Padgett, Adrian L Breto, Matthew C Abramowitz, Alan Dal Pra, Alan Pollack, Radka Stoyanova
In radiotherapy (RT) of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate tumor habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated. Other issues in the treatment of the RT patient include the choice of the RT technique (hypo- or standard fractionation) and the use and length of concurrent/adjuvant androgen deprivation therapy (ADT). Up to 50% of high-risk men demonstrate biochemical failure suggesting that additional strategies for defining and treating patients based on improved risk stratification are required...
June 2018: Translational Andrology and Urology
Bo Dai, Lu He, Lulu Zheng, Yongfeng Fu, Kaimin Wang, Guodong Sui, Dawei Zhang, Songlin Zhuang, Xu Wang
Ultrafast time-stretch imaging technique recently attracts an increasing interest for applications in cell classification due to high throughput and high sensitivity. A novel imaging modality of time-stretch imaging technique for edge detection is proposed. Edge detection based on the directional derivative is realized using differential detection. As the image processing is mainly implemented in the physical layer, the computation complexity of edge extraction is significantly reduced. An imaging system for edge detection with the scan rate of 77...
July 10, 2018: Journal of Biophotonics
Robherson Wector de Sousa Costa, Giovanni Lucca França da Silva, Antonio Oseas de Carvalho Filho, Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva, Marcelo Gattass
Lung cancer presents the highest cause of death among patients around the world, in addition of being one of the smallest survival rates after diagnosis. Therefore, this study proposes a methodology for diagnosis of lung nodules in benign and malignant tumors based on image processing and pattern recognition techniques. Mean phylogenetic distance (MPD) and taxonomic diversity index (Δ) were used as texture descriptors. Finally, the genetic algorithm in conjunction with the support vector machine were applied to select the best training model...
November 2018: Medical & Biological Engineering & Computing
Niloufar Zarinabad, Emma M Meeus, Karen Manias, Katharine Foster, Andrew Peet
BACKGROUND: Advances in magnetic resonance imaging and the introduction of clinical decision support systems has underlined the need for an analysis tool to extract and analyze relevant information from magnetic resonance imaging data to aid decision making, prevent errors, and enhance health care. OBJECTIVE: The aim of this study was to design and develop a modular medical image region of interest analysis tool and repository (MIROR) for automatic processing, classification, evaluation, and representation of advanced magnetic resonance imaging data...
May 2, 2018: JMIR Medical Informatics
Ramón Rami-Porta, Sergi Call, Christophe Dooms, Carme Obiols, Marcelo Sánchez, William D Travis, Ivan Vollmer
Diagnosis and clinical staging of lung cancer are fundamental to planning therapy. The techniques for clinical staging, i.e anatomic and metabolic imaging, endoscopies and minimally invasive surgical procedures, should be performed sequentially and with an increasing degree of invasiveness. Intraoperative staging, assessing the magnitude of the primary tumour, the involved structures, and the loco-regional lymphatic spread by means of systematic nodal dissection, is essential in order to achieve a complete resection...
May 2018: European Respiratory Journal: Official Journal of the European Society for Clinical Respiratory Physiology
Irene Fondón, Auxiliadora Sarmiento, Ana Isabel García, María Silvestre, Catarina Eloy, António Polónia, Paulo Aguiar
Breast cancer is the second leading cause of cancer death among women. Its early diagnosis is extremely important to prevent avoidable deaths. However, malignancy assessment of tissue biopsies is complex and dependent on observer subjectivity. Moreover, hematoxylin and eosin (H&E)-stained histological images exhibit a highly variable appearance, even within the same malignancy level. In this paper, we propose a computer-aided diagnosis (CAD) tool for automated malignancy assessment of breast tissue samples based on the processing of histological images...
May 1, 2018: Computers in Biology and Medicine
João Otávio Bandeira Diniz, Pedro Henrique Bandeira Diniz, Thales Levi Azevedo Valente, Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva, Marcelo Gattass
BACKGROUND AND OBJECTIVE: The processing of medical image is an important tool to assist in minimizing the degree of uncertainty of the specialist, while providing specialists with an additional source of detect and diagnosis information. Breast cancer is the most common type of cancer that affects the female population around the world. It is also the most deadly type of cancer among women. It is the second most common type of cancer among all others. The most common examination to diagnose breast cancer early is mammography...
March 2018: Computer Methods and Programs in Biomedicine
E Udayakumar, S Santhi, P Vetrivelan
Context: Breast cancer is a biggest threat to women. X-ray mammography is the most effective method for early detection and screening of breast cancer. It is a tough challenge for the radiologist in reading mammography since it does not provide consistent result every time. Aim: To improve the primary sign of this disease, computer-aided diagnosis schemes have been developed. Using monitor, digital images of mammography are displayed and they can be lightened or darkened before they are printed on the film...
July 2017: Indian Journal of Medical and Paediatric Oncology
S Saraswathi, L Mary Immaculate Sheela
Detection of lung cancer through image processing is an important tool for diagnosis. In recent years, image processing techniques have become more widely used. Lung segmentation is an essential pre-processing step for most (CAD) schemes. An automated system is proposed in this paper for identifying lung cancer from the analysis of computed tomography images by performing nodule segmentation using an optimal critical point selection algorithm (OCPS) which improves the detection of shape- and size-based juxtapleural nodules located at the lung boundary...
November 26, 2017: Asian Pacific Journal of Cancer Prevention: APJCP
Philipp Kainz, Michael Pfeiffer, Martin Urschler
Segmentation of histopathology sections is a necessary preprocessing step for digital pathology. Due to the large variability of biological tissue, machine learning techniques have shown superior performance over conventional image processing methods. Here we present our deep neural network-based approach for segmentation and classification of glands in tissue of benign and malignant colorectal cancer, which was developed to participate in the GlaS@MICCAI2015 colon gland segmentation challenge. We use two distinct deep convolutional neural networks (CNN) for pixel-wise classification of Hematoxylin-Eosin stained images...
2017: PeerJ
Javad Sovizi, Kelsey B Mathieu, Sara L Thrower, Wolfgang Stefan, John D Hazle, David Fuentes
MOTIVATION: Superparamagnetic relaxometry (SPMR) is an emerging technology that holds potential for use in early cancer detection. Measurement of the magnetic field after the excitation of cancer-bound superparamagnetic iron oxide nanoparticles (SPIONs) enables the reconstruction of SPIONs spatial distribution and hence tumor detection. However, image reconstruction often requires solving an ill-posed inverse problem that is computationally challenging and sensitive to measurement uncertainty...
October 2017: Artificial Intelligence in Medicine
Atsushi Teramoto, Tetsuya Tsukamoto, Yuka Kiriyama, Hiroshi Fujita
Lung cancer is a leading cause of death worldwide. Currently, in differential diagnosis of lung cancer, accurate classification of cancer types (adenocarcinoma, squamous cell carcinoma, and small cell carcinoma) is required. However, improving the accuracy and stability of diagnosis is challenging. In this study, we developed an automated classification scheme for lung cancers presented in microscopic images using a deep convolutional neural network (DCNN), which is a major deep learning technique. The DCNN used for classification consists of three convolutional layers, three pooling layers, and two fully connected layers...
2017: BioMed Research International
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