Rashmi et al., 2022 - Google Patents
Breast histopathological image analysis using image processing techniques for diagnostic purposes: A methodological reviewRashmi et al., 2022
View HTML- Document ID
- 14209907630467229039
- Author
- Rashmi R
- Prasad K
- Udupa C
- Publication year
- Publication venue
- Journal of Medical Systems
External Links
Snippet
Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However …
- 238000000034 method 0 title abstract description 78
Classifications
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- 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
- G06T2207/30024—Cell structures in vitro; Tissue sections in vitro
-
- 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
- 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
- G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
- G06K9/00147—Matching; Classification
-
- 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
- G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
- G06K9/0014—Pre-processing, e.g. image segmentation ; Feature extraction
-
- 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
-
- 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
-
- 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
- G06T2207/10024—Color 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
- G06T2207/20092—Interactive image processing based on input by user
- G06T2207/20104—Interactive definition of region of interest [ROI]
-
- 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
- G06K9/20—Image acquisition
- G06K9/34—Segmentation of touching or overlapping patterns in the image field
- G06K9/342—Cutting or merging image elements, e.g. region growing, watershed, clustering-based techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Rashmi et al. | Breast histopathological image analysis using image processing techniques for diagnostic purposes: A methodological review | |
Salvi et al. | The impact of pre-and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis | |
Kaushal et al. | Recent trends in computer assisted diagnosis (CAD) system for breast cancer diagnosis using histopathological images | |
Li et al. | Classification of breast cancer histology images using multi-size and discriminative patches based on deep learning | |
Hamidinekoo et al. | Deep learning in mammography and breast histology, an overview and future trends | |
Jimenez-del-Toro et al. | Analysis of histopathology images: From traditional machine learning to deep learning | |
Kromp et al. | Evaluation of deep learning architectures for complex immunofluorescence nuclear image segmentation | |
Abbasniya et al. | Classification of breast tumors based on histopathology images using deep features and ensemble of gradient boosting methods | |
Kumar et al. | A dataset and a technique for generalized nuclear segmentation for computational pathology | |
Sun et al. | Gastric histopathology image segmentation using a hierarchical conditional random field | |
AlZubaidi et al. | Computer aided diagnosis in digital pathology application: Review and perspective approach in lung cancer classification | |
Prabhu et al. | AI-based carcinoma detection and classification using histopathological images: A systematic review | |
He et al. | A review: The detection of cancer cells in histopathology based on machine vision | |
Bhattacharjee et al. | Review on histopathological slide analysis using digital microscopy | |
Mehta et al. | End-to-end diagnosis of breast biopsy images with transformers | |
Matias et al. | What is the state of the art of computer vision-assisted cytology? A Systematic Literature Review | |
Hu et al. | Breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy | |
Tsaku et al. | Texture-based deep learning for effective histopathological cancer image classification | |
Dabass et al. | A hybrid U-Net model with attention and advanced convolutional learning modules for simultaneous gland segmentation and cancer grade prediction in colorectal histopathological images | |
Kloeckner et al. | Multi-categorical classification using deep learning applied to the diagnosis of gastric cancer | |
Pedersen et al. | H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images | |
Baroni et al. | Optimizing Vision Transformers for Histopathology: Pretraining and Normalization in Breast Cancer Classification | |
Saranyaraj et al. | Early prediction of breast cancer based on the classification of HER‐2 and ER biomarkers using deep neural network | |
Alzubaidi et al. | Multi-class breast cancer classification by a novel two-branch deep convolutional neural network architecture | |
Mehak et al. | Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder. |