Lay-Ekuakille et al., 2016 - Google Patents
Measuring lung abnormalities in images-based CTLay-Ekuakille et al., 2016
View PDF- Document ID
- 16739293262295575705
- Author
- Lay-Ekuakille A
- Vergallo P
- Jabłoński I
- Casciaro S
- Conversano F
- Publication year
- Publication venue
- International Journal on Smart Sensing and Intelligent Systems
External Links
Snippet
Diagnosis by imaging is one of the most important findings in biomedical imaging because it allows not only the diagnosing of a specific pathology but to perform online and offline surgical operations using imaging as it is noticed in interventional radiology. This paper …
- 210000004072 Lung 0 title abstract description 31
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
- G06T7/0014—Biomedical image inspection using an image reference approach
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10104—Positron emission tomography [PET]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10132—Ultrasound image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10116—X-ray image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
- G06T2207/20156—Automatic seed setting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K2209/05—Recognition of patterns in medical or anatomical images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/0031—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for topological mapping of a higher dimensional structure on a lower dimensional surface
- G06T3/0037—Reshaping or unfolding a 3D tree structure onto a 2D plane
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US8345940B2 (en) | Method and system for automatic processing and evaluation of images, particularly diagnostic images | |
Chawla et al. | A method for automatic detection and classification of stroke from brain CT images | |
Ganeshan et al. | Texture analysis in non-contrast enhanced CT: impact of malignancy on texture in apparently disease-free areas of the liver | |
Ortiz-Ramón et al. | Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics approach | |
US8090172B2 (en) | Robust segmentation of breast and muscle in MRI | |
AU2005207310B2 (en) | System and method for filtering a medical image | |
Wang et al. | Fully automated segmentation of the pectoralis muscle boundary in breast MR images | |
Caresio et al. | Quantitative analysis of thyroid tumors vascularity: A comparison between 3‐D contrast‐enhanced ultrasound and 3‐D Power Doppler on benign and malignant thyroid nodules | |
US20090069665A1 (en) | Automatic Lesion Correlation in Multiple MR Modalities | |
Chen et al. | Computer-aided diagnosis in breast ultrasound | |
Lay-Ekuakille et al. | Measuring lung abnormalities in images-based CT | |
Biltawi et al. | Mammogram enhancement and segmentation methods: classification, analysis, and evaluation | |
Ding et al. | A novel wavelet-transform-based convolution classification network for cervical lymph node metastasis of papillary thyroid carcinoma in ultrasound images | |
Joshi et al. | Automatic liver tumour detection in abdominal ct images | |
Ahmed et al. | Detection of uterine fibroids in medical images using deep neural networks | |
Wei et al. | Automatic detection of nodules attached to vessels in lung CT by volume projection analysis | |
Ertas et al. | Computerized detection of breast lesions in multi-centre and multi-instrument DCE-MR data using 3D principal component maps and template matching | |
Venkatachalam et al. | Processing of abdominal ultrasound images using seed based region growing method | |
Susomboon et al. | A co-occurrence texture semi-invariance to direction, distance, and patient size | |
Sprindzuk et al. | Lung cancer differential diagnosis based on the computer assisted radiology: The state of the art | |
Somasundaram | Machine Learning Algorithm for Abnormality Detection and Classification of Kidney Stones in Ultrasound Images | |
Hassanien et al. | Contrast enhancement of breast MRI images based on fuzzy type-II | |
US20090069669A1 (en) | Efficient Features for Detection of Motion Artifacts in Breast MRI | |
Sahoo et al. | Boundary detection of biomedical images using modified morphological operation | |
Patel et al. | Reliable Computer-aided Diagnosis System using Region based Segmentation of Mammographic Breast Cancer Images |