Di Ruberto et al., 2016 - Google Patents
A leucocytes count system from blood smear images: Segmentation and counting of white blood cells based on learning by samplingDi Ruberto et al., 2016
View PDF- Document ID
- 1915310496146562139
- Author
- Di Ruberto C
- Loddo A
- Putzu L
- Publication year
- Publication venue
- Machine Vision and Applications
External Links
Snippet
Automated blood cell counting instruments are very important tools, daily used by haematologists and medical analysts to perform a complete blood count (CBC). The results of the CBC may be complex to interpret but could lead to important decisions regarding the …
- 210000002865 immune cell 0 title abstract description 70
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
- G06K9/36—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
- G06K9/62—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
- G06K9/00127—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
- 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
- G06T2207/20112—Image segmentation details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Di Ruberto et al. | A leucocytes count system from blood smear images: Segmentation and counting of white blood cells based on learning by sampling | |
Bibin et al. | Malaria parasite detection from peripheral blood smear images using deep belief networks | |
Moshavash et al. | An automatic and robust decision support system for accurate acute leukemia diagnosis from blood microscopic images | |
Acharya et al. | Detection of acute lymphoblastic leukemia using image segmentation and data mining algorithms | |
Ramesh et al. | Isolation and two-step classification of normal white blood cells in peripheral blood smears | |
Putzu et al. | Leucocyte classification for leukaemia detection using image processing techniques | |
Khamael et al. | Segmentation of white blood cell, nucleus and cytoplasm in digital haematology microscope images: A review–challenges, current and future potential techniques | |
Rezatofighi et al. | Automatic recognition of five types of white blood cells in peripheral blood | |
Kazemi et al. | Automatic recognition of acute myelogenous leukemia in blood microscopic images using k-means clustering and support vector machine | |
Mariarputham et al. | Nominated texture based cervical cancer classification | |
Madhloom et al. | An image processing application for the localization and segmentation of lymphoblast cell using peripheral blood images | |
Pan et al. | Cell detection in pathology and microscopy images with multi-scale fully convolutional neural networks | |
Habibzadeh et al. | Comparative study of shape, intensity and texture features and support vector machine for white blood cell classification | |
Shirazi et al. | Extreme learning machine based microscopic red blood cells classification | |
Safdar et al. | Intelligent microscopic approach for identification and recognition of citrus deformities | |
Hagos et al. | ConCORDe-Net: cell count regularized convolutional neural network for cell detection in multiplex immunohistochemistry images | |
Mishra et al. | Glrlm-based feature extraction for acute lymphoblastic leukemia (all) detection | |
Hegde et al. | Image processing approach for detection of leukocytes in peripheral blood smears | |
Elsalamony | Anaemia cells detection based on shape signature using neural networks | |
Umamaheswari et al. | Review on image segmentation techniques incorporated with machine learning in the scrutinization of leukemic microscopic stained blood smear images | |
Rahman et al. | Automatic detection of white blood cells from microscopic images for malignancy classification of acute lymphoblastic leukemia | |
Razavi et al. | MiNuGAN: Dual segmentation of mitoses and nuclei using conditional GANs on multi-center breast H&E images | |
Shihabuddin et al. | Multi CNN based automatic detection of mitotic nuclei in breast histopathological images | |
Mbiki et al. | Classifying changes in LN-18 glial cell morphology: a supervised machine learning approach to analyzing cell microscopy data via FIJI and WEKA | |
Sapna et al. | Techniques for segmentation and classification of leukocytes in blood smear images-a review |