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Search Results (19)
  • Open Access

    ARTICLE

    Automatic Liver Tumor Segmentation in CT Modalities Using MAT-ACM

    S. Priyadarsini1,*, Carlos Andrés Tavera Romero2, Abolfazl Mehbodniya3, P. Vidya Sagar4, Sudhakar Sengan5

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1057-1068, 2022, DOI:10.32604/csse.2022.024788

    Abstract In the recent days, the segmentation of Liver Tumor (LT) has been demanding and challenging. The process of segmenting the liver and accurately spotting the tumor is demanding due to the diversity of shape, texture, and intensity of the liver image. The intensity similarities of the neighboring organs of the liver create difficulties during liver segmentation. The manual segmentation does not provide an accurate segmentation because the results provided by different medical experts can vary. Also, this manual technique requires a large number of image slices and time for segmentation. To solve these issues, the Fully Automatic Segmentation (FAS) technique… More >

  • Open Access

    ARTICLE

    Brain Tumor Segmentation using Multi-View Attention based Ensemble Network

    Noreen Mushtaq1, Arfat Ahmad Khan2, Faizan Ahmed Khan3, Muhammad Junaid Ali4, Malik Muhammad Ali Shahid5, Chitapong Wechtaisong2,*, Peerapong Uthansakul2

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5793-5806, 2022, DOI:10.32604/cmc.2022.024316

    Abstract Astrocytoma IV or glioblastoma is one of the fatal and dangerous types of brain tumors. Early detection of brain tumor increases the survival rate and helps in reducing the fatality rate. Various imaging modalities have been used for diagnosing by expert radiologists, and Medical Resonance Image (MRI) is considered a better option for detecting brain tumors as MRI is a non-invasive technique and provides better visualization of the brain region. One of the challenging issues is to identify the tumorous region from the MRI scans correctly. Manual segmentation is performed by medical experts, which is a time-consuming task and got… More >

  • Open Access

    ARTICLE

    MRI Brain Tumor Segmentation with Intuitionist Possibilistic Fuzzy Clustering and Morphological Operations

    J. Anitha*, M. Kalaiarasu

    Computer Systems Science and Engineering, Vol.43, No.1, pp. 363-379, 2022, DOI:10.32604/csse.2022.022402

    Abstract Digital Image Processing (DIP) is a well-developed field in the biological sciences which involves classification and detection of tumour. In medical science, automatic brain tumor diagnosis is an important phase. Brain tumor detection is performed by Computer-Aided Diagnosis (CAD) systems. The human image creation is greatly achieved by an approach namely medical imaging which is exploited for medical and research purposes. Recently Automatic brain tumor detection from MRI images has become the emerging research area of medical research. Brain tumor diagnosis mainly performed for obtaining exact location, orientation and area of abnormal tissues. Cancer and edema regions inference from brain… More >

  • Open Access

    ARTICLE

    Efficient Computer Aided Diagnosis System for Hepatic Tumors Using Computed Tomography Scans

    Yasmeen Al-Saeed1,2, Wael A. Gab-Allah1, Hassan Soliman1, Maysoon F. Abulkhair3, Wafaa M. Shalash4, Mohammed Elmogy1,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4871-4894, 2022, DOI:10.32604/cmc.2022.023638

    Abstract One of the leading causes of mortality worldwide is liver cancer. The earlier the detection of hepatic tumors, the lower the mortality rate. This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors. Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range, intensity values overlap between the liver and neighboring organs, high noise from computed tomography scanner, and large variance in tumors shapes. The proposed method consists of three main stages; liver segmentation using Fast… More >

  • Open Access

    ARTICLE

    Kidney Tumor Segmentation Using Two-Stage Bottleneck Block Architecture

    Fuat Turk1,*, Murat Luy2, Necaattin Barışçı3, Fikret Yalçınkaya4

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 349-363, 2022, DOI:10.32604/iasc.2022.023710

    Abstract Cases of kidney cancer have shown a rapid increase in recent years. Advanced technology has allowed bettering the existing treatment methods. Research on the subject is still continuing. Medical segmentation is also of increasing importance. In particular, deep learning-based studies are of great importance for accurate segmentation. Tumor detection is a relatively difficult procedure for soft tissue organs such as kidneys and the prostate. Kidney tumors, specifically, are a type of cancer with a higher incidence in older people. As age progresses, the importance of having diagnostic tests increases. In some cases, patients with kidney tumors may not show any… More >

  • Open Access

    ARTICLE

    MRI Brain Tumor Segmentation Using 3D U-Net with Dense Encoder Blocks and Residual Decoder Blocks

    Juhong Tie1,2,*, Hui Peng2, Jiliu Zhou1,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 427-445, 2021, DOI:10.32604/cmes.2021.014107

    Abstract The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automatically segment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancing tumor core from 3D MR images. Because the location, size, shape and intensity of brain tumors vary greatly, it is very difficult to segment these brain tumor regions automatically. In this paper, by combining the advantages of DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder blocks. We used dense blocks in the encoder part and residual blocks in the decoder part. The number… More >

  • Open Access

    ARTICLE

    Residual U-Network for Breast Tumor Segmentation from Magnetic Resonance Images

    Ishu Anand1, Himani Negi1, Deepika Kumar1, Mamta Mittal2, Tai-hoon Kim3,*, Sudipta Roy4

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3107-3127, 2021, DOI:10.32604/cmc.2021.014229

    Abstract Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world. It is apical of all new cancer incidences analyzed among females. Two features substantially influence the classification accuracy of malignancy and benignity in automated cancer diagnostics. These are the precision of tumor segmentation and appropriateness of extracted attributes required for the diagnosis. In this research, the authors have proposed a ResU-Net (Residual U-Network) model for breast tumor segmentation. The proposed methodology renders augmented, and precise identification of tumor regions and produces accurate breast tumor segmentation in contrast-enhanced MR images.… More >

  • Open Access

    ARTICLE

    Automatic Segmentation of Liver from Abdominal Computed Tomography Images Using Energy Feature

    Prabakaran Rajamanickam1, Shiloah Elizabeth Darmanayagam1,*, Sunil Retmin Raj Cyril Raj2

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 709-722, 2021, DOI:10.32604/cmc.2021.014347

    Abstract Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography (CT) images. The segmentation of hepatic organ is more intricate task, owing to the fact that it possesses a sizeable quantum of vascularization. This paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT scans. The effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or not. This involves segmentation of the region of interest (ROI) from… More >

  • Open Access

    ARTICLE

    Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model

    Ihsan Jasim Hussein1, M. A. Burhanuddin2, Mazin Abed Mohammed3,*, Mohamed Elhoseny4, Begonya Garcia-Zapirain5, Marwah Suliman Maashi6, Mashael S. Maashi7

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3161-3182, 2021, DOI:10.32604/cmc.2021.012691

    Abstract One of the most complex tasks for computer-aided diagnosis (Intelligent decision support system) is the segmentation of lesions. Thus, this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images. The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases. In addition, proposed an approach that can efficiently generate region-of-interest (ROI) and new features that can be used in characterizing lesion boundaries. This study uses two databases in training and testing the proposed segmentation approach. The breast cancer… More >

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