18 papers · ranked by Valyu relevance
Ramin Ranjbarzadeh, Abbas Bagherian Kasgari, Saeid Jafarzadeh Ghoushchi, Shokofeh Anari + 2 more
'Saeid Jafarzadeh Ghoushchi' 'Shokofeh Anari' 'Maryam Naseri' 'Malika Bendechache'] Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related…
Myungeun Lee, Jong Hyo Kim, Wookjin Choi, Ki Hong Lee
TumorPrism3D software was developed to segment brain tumors with a straightforward and user-friendly graphical interface applied to two- and three-dimensional brain magnetic resonance (MR) images. The MR images of 185 patients (103 males, 82 females) with glioblastoma multiforme were downloaded from The Cancer Imaging…
Weiwei Wu, Shuicai Wu, Zhuhuang Zhou, Rui Zhang + 1 more
Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, an efficient semiautomatic…
S. M. Kamrul Hasan, Mohiuddin Ahmad
Though the modern medical imaging research is advancing at a booming rate, it is still a very challenging task to detect brain tumor perfectly. Medical imaging unlike other imaging system has highest penalty for a minimal error. So, the detection of tumor should be accurate to minimize the error. Past researchers used…
K. Ruwani M. Fernando, Chris P. Tsokos
Clinical diagnosis and treatment decisions rely upon the integration of patient-specific data with clinical reasoning. Cancer presents a unique context that influences treatment decisions, given its diverse forms of disease evolution. Biomedical imaging allows non-invasive assessment of diseases based on visual…
Khurram Ejaz, Mohd Shafry Mohd Rahim, Muhammad Arif, Diana Izdrui + 2 more
Modalities like MRI give information about organs and highlight diseases. Organ information is visualized in intensities. The segmentation method plays an important role in the identification of the region of interest (ROI). The ROI can be segmented from the image using clustering, features, and region extraction.…
Reham Kaifi, Dechang Chen, Wan Azani Mustafa, Hiam Alquran
Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important to save many lives. Brain tumors can be divided into several categories depending on the kind, place of origin, pace of development, and stage of progression; as a result, tumor classification is crucial…
Muhammad Ali Qadar, Zhaowen Yan
Five different threshold segmentation based approaches have been reviewed and compared over here to extract the tumor from set of brain images. This research focuses on the analysis of image segmentation methods, a comparison of five semi-automated methods have been undertaken for evaluating their relative performance…
Chandan Ganesh Bangalore Yogananda, Sahil S. Nalawade, Gowtham K. Murugesan, Ben Wagner + 4 more
Tumor segmentation of magnetic resonance (MR) images is a critical step in providing objective measures of predicting aggressiveness and response to therapy in gliomas. It has valuable applications in diagnosis, monitoring, and treatment planning of brain tumors. The purpose of this work was to develop a fully…
Ahmeed Suliman Farhan, Muhammad Khalid, Umar Manzoor
Brain tumor segmentation from Magnetic Resonance Images (MRI) presents significant challenges due to the complex nature of brain tumor tissues. This complexity makes distinguishing tumor tissues from healthy tissues difficult, mainly when radiologists perform manual segmentation. Reliable and accurate segmentation is…
Pranay Manocha, Snehal Bhasme, Tanvi Gupta, Bijaya Ketan Panigrahi + 1 more
'Tapan Kumar Gandhi'] Abstract. Magnetic Resonance Imaging (MRI) is an important diagnostic tool for precise detection of various pathologies. Magnetic Resonance (MR) is more preferred than Computed Tomography (CT) due to the high resolution in MR images which help in better detection of neurological conditions.…
M. Jalili Aziz, A. Amiri Tehrani Zade, P. Farnia, M. Alimohamadi + 3 more
Glioma is a highly invasive type of brain tumor that appears in different parts of brain with various sizes, shapes, and blurred borders. Therefore, it is a challenging task to identify the exact boundaries of the tumor in an MR image. In recent years, deep learning based CNNs methods have gained popularity in the…
Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Deepta Rajan, Anup Puri + 2 more
'Deepta Rajan' 'Anup Puri' 'David Frakes' 'Andreas Spanias'] In this paper, we propose sparse coding-based approaches for segmentation of tumor regions from MR images. Sparse coding with data-adapted dictionaries has been successfully employed in several image recovery and vision problems. The proposed approaches…
Joonsang Lee, Qun Zhao, Marc Kent, Simon Platt
In this study, we developed the temporal independent component analysis (tICA) to solve the partial volume effect (PVE) in canine brain tumor segmentation. The performance of the tICA is compared to that of spatial ICA (sICA) and an expert manual delineation of tumors based on three criteria: percent volume overlap or…
Sudipta Roy, Sanjay Nag, Indra Kanta Maitra, Samir Kumar Bandyopadhyay
'Samir Kumar Bandyopadhyay'] > 1 Department of Computer Science and Engineering, University of Calcutta, 92 A.P.C. Road, Kolkata-700009, India. 3 Research Scholar, Department of Computer Science & Engineering, University of Calcutta, India. 2 Research Fellow, Department of Computer Science & Engineering, University of…
Mehwish Rasheed, Muhammad Waseem Iqbal, Arfan Jaffar, Muhammad Usman Ashraf + 4 more
'Muhammad Usman Ashraf' 'Khalid Ali Almarhabi' 'Ahmed Mohammed Alghamdi' 'Adel A. Bahaddad' 'Fabiano Bini'] The human brain, primarily composed of white blood cells, is centered on the neurological system. Incorrectly positioned cells in the immune system, blood vessels, endocrine, glial, axon, and other cancer-causing…
Xian-Xian Liu, Gloria Li, Wei Luo, Juntao Gao + 1 more
Detection and classification of gastric bleeding tissues are one of the challenging tasks in endoscopy image analysis. Lesion detection plays an important role in gastric cancer (GC) diagnosis and follow-up. Manual segmentation of endoscopy images is a very time-consuming task and subject to intra- and interrater…
Authors not listed
Fluorescence imaging in the second near-infrared window (NIR-II, 1000–1700 nm) has emerged as a powerful tool for in vivo bioimaging, offering deep-tissue penetration alongside superior spatiotemporal resolution and contrast. The development of high-performance probes is essential for achieving high-quality imaging…