A framework for automated tumor detection in thoracic FDG pet images using texture-based features

Saradhi, G. Vijaya; Gopalakrishnan, Girish; Roy, Ankit; Mullick, Rakesh; Manjeshwar, Ravindra; Thielemans, Kris; Patil, Uday

doi:10.1109/isbi.2009.5192992

Cited by 18 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another category of lesion detection is to detect all instances from PET images, regardless of their types. Such approaches include a texture-based classification method, 10 and a water-shed based algorithm integrated with morphological measures. 11 A common drawback with these techniques is that they operate on user-selected volume-of-interest (VOI) or potential lesions.…”

Section: Review Of State-of-the-artmentioning

confidence: 99%

Feature-Centric Lesion Detection and Retrieval in Thoracic Images

Song¹,

Cai²,

Eberl³

et al. 2013

Computer Vision in Medical Imaging

View full text Add to dashboard Cite

Advances in medical digital imaging have greatly benefited patient care. Computer-aided diagnosis is increasingly being used to facilitate semi-or fullyautomatic medical image analysis and image retrieval. While different tasks involve different methodologies in this domain, these tasks normally require image feature extraction as an essential component in the algorithmic framework. In this Chapter, we focus on image feature modeling in lesion detection and image retrieval for thoracic images. For both tasks, we first review the state-of-the-art and then present some of our own work in more detail.

show abstract

Section: Review Of State-of-the-artmentioning

confidence: 99%

Feature-Centric Lesion Detection and Retrieval in Thoracic Images

Song¹,

Cai²,

Eberl³

et al. 2013

Computer Vision in Medical Imaging

View full text Add to dashboard Cite

show abstract

“…Clinically, in order to better analyze and judge the disease condition, doctors often need to study the segmented cell images to observe the shape, size, and other properties of the cells and how they changed under different conditions. Many medical tasks involve the image segmentation, such as tube detection [2], brain development study [3], heart segmentation and analysis of cardiac images [4]. Therefore, how to achieve accurate segmentation of images is a popular research problem.…”

Section: Introductionmentioning

confidence: 99%

Medical Image Segmentation Based on U-Net

Chen

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Traditional statistical methods, although having a solid theoretical foundation, have been challenged in terms of their efficiency as well as their generalization ability in the face of the ever-increasing amount of massive data. With the rise of deep learning in recent years, the use of new tools such as convolutional neural networks to get information from data has become a new option. In particular, in the field of imaging, segmentation of medical images is important for tasks such as determining the type of disease and the location of lesions, which are excellent application areas for deep learning. U-Net is a particularly important deep model structure with good results for segmentation of medical images. However, there is a lack of discussions on the application of U-Net in the clinical field. In this paper, we introduce traditional image segmentation methods and U-Net, analyze the advantages of deep learning techniques in the field of image segmentation. In addition, we applied U-Net to the problem of cell segmentation and segmentation of covid-19 CT images, showing the potential of U-Net for clinical applications.

show abstract

“…This has been shown in multiple modalities including ultrasound [19] and MRI [20]. PET and CT textures in the lung have been used in a large number of applications such as differentiating malignant from benign lymph nodes [21, 22], judging treatment response [23], diagnosing diffuse parenchymal lung disease [24–26], determining tumor staging [27], detection [28], and segmentation [29]. However, these applications have used the textures from each modality independently.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT

Markel

Caldwell

Alasti

et al. 2013

International Journal of Molecular Imaging

View full text Add to dashboard Cite

Target definition is the largest source of geometric uncertainty in radiation therapy. This is partly due to a lack of contrast between tumor and healthy soft tissue for computed tomography (CT) and due to blurriness, lower spatial resolution, and lack of a truly quantitative unit for positron emission tomography (PET). First-, second-, and higher-order statistics, Tamura, and structural features were characterized for PET and CT images of lung carcinoma and organs of the thorax. A combined decision tree (DT) with K-nearest neighbours (KNN) classifiers as nodes containing combinations of 3 features were trained and used for segmentation of the gross tumor volume. This approach was validated for 31 patients from two separate institutions and scanners. The results were compared with thresholding approaches, the fuzzy clustering method, the 3-level fuzzy locally adaptive Bayesian algorithm, the multivalued level set algorithm, and a single KNN using Hounsfield units and standard uptake value. The results showed the DTKNN classifier had the highest sensitivity of 73.9%, second highest average Dice coefficient of 0.607, and a specificity of 99.2% for classifying voxels when using a probabilistic ground truth provided by simultaneous truth and performance level estimation using contours drawn by 3 trained physicians.

show abstract

A framework for automated tumor detection in thoracic FDG pet images using texture-based features

Cited by 18 publications

References 11 publications

Feature-Centric Lesion Detection and Retrieval in Thoracic Images

Feature-Centric Lesion Detection and Retrieval in Thoracic Images

Medical Image Segmentation Based on U-Net

Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT

Contact Info

Product

Resources

About