248 research outputs found
Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review
Instance segmentation of nuclei and glands in the histology images is an
important step in computational pathology workflow for cancer diagnosis,
treatment planning and survival analysis. With the advent of modern hardware,
the recent availability of large-scale quality public datasets and the
community organized grand challenges have seen a surge in automated methods
focusing on domain specific challenges, which is pivotal for technology
advancements and clinical translation. In this survey, 126 papers illustrating
the AI based methods for nuclei and glands instance segmentation published in
the last five years (2017-2022) are deeply analyzed, the limitations of current
approaches and the open challenges are discussed. Moreover, the potential
future research direction is presented and the contribution of state-of-the-art
methods is summarized. Further, a generalized summary of publicly available
datasets and a detailed insights on the grand challenges illustrating the top
performing methods specific to each challenge is also provided. Besides, we
intended to give the reader current state of existing research and pointers to
the future directions in developing methods that can be used in clinical
practice enabling improved diagnosis, grading, prognosis, and treatment
planning of cancer. To the best of our knowledge, no previous work has reviewed
the instance segmentation in histology images focusing towards this direction.Comment: 60 pages, 14 figure
Red blood cell segmentation and classification method using MATLAB
Red blood cells (RBCs) are the most important kind of blood cell. Its diagnosis is very
important process for early detection of related disease such as malaria and anemia before
suitable follow up treatment can be proceed. Some of the human disease can be showed
by counting the number of red blood cells. Red blood cell count gives the vital information
that help diagnosis many of the patient’s sickness. Conventional method under blood
smears RBC diagnosis is applying light microscope conducted by pathologist. This
method is time-consuming and laborious. In this project an automated RBC counting is
proposed to speed up the time consumption and to reduce the potential of the wrongly
identified RBC. Initially the RBC goes for image pre-processing which involved global
thresholding. Then it continues with RBCs counting by using two different algorithms
which are the watershed segmentation based on distance transform, and the second one is
the artificial neural network (ANN) classification with fitting application depend on
regression method. Before applying ANN classification there are step needed to get
feature extraction data that are the data extraction using moment invariant. There are still
weaknesses and constraints due to the image itself such as color similarity, weak edge
boundary, overlapping condition, and image quality. Thus, more study must be done to
handle those matters to produce strong analysis approach for medical diagnosis purpose.
This project build a better solution and help to improve the current methods so that it can
be more capable, robust, and effective whenever any sample of blood cell is analyzed. At
the end of this project it conducted comparison between 20 images of blood samples taken
from the medical electronic laboratory in Universiti Tun Hussein Onn Malaysia (UTHM).
The proposed method has been tested on blood cell images and the effectiveness and
reliability of each of the counting method has been demonstrated
Automatic segmentation of overlapping cervical smear cells based on local distinctive features and guided shape deformation
Automated segmentation of cells from cervical smears poses great challenge to biomedical image analysis because of the noisy and complex background, poor cytoplasmic contrast and the presence of fuzzy and overlapping cells. In this paper, we propose an automated segmentation method for the nucleus and cytoplasm in a cluster of cervical cells based on distinctive local features and guided sparse shape deformation. Our proposed approach is performed in two stages: segmentation of nuclei and cellular clusters, and segmentation of overlapping cytoplasm. In the rst stage, a set of local discriminative shape and appearance cues of image superpixels is incorporated and classi ed by the Support Vector Machine (SVM) to segment the image into nuclei, cellular clusters, and background. In the second stage, a robust shape deformation framework is proposed, based on Sparse Coding (SC) theory and guided by representative shape features, to construct the cytoplasmic shape of each overlapping cell. Then, the obtained shape is re ned by the Distance Regularized Level Set Evolution (DRLSE) model. We evaluated our approach using the ISBI 2014 challenge dataset, which has 135 synthetic cell images for a total of 810 cells. Our results show that our approach outperformed existing approaches in segmenting overlapping cells and obtaining accurate nuclear boundaries. Keywords: overlapping cervical smear cells, feature extraction, sparse coding, shape deformation, distance regularized level set
A Review on Classification of White Blood Cells Using Machine Learning Models
The machine learning (ML) and deep learning (DL) models contribute to
exceptional medical image analysis improvement. The models enhance the
prediction and improve the accuracy by prediction and classification. It helps
the hematologist to diagnose the blood cancer and brain tumor based on
calculations and facts. This review focuses on an in-depth analysis of modern
techniques applied in the domain of medical image analysis of white blood cell
classification. For this review, the methodologies are discussed that have used
blood smear images, magnetic resonance imaging (MRI), X-rays, and similar
medical imaging domains. The main impact of this review is to present a
detailed analysis of machine learning techniques applied for the classification
of white blood cells (WBCs). This analysis provides valuable insight, such as
the most widely used techniques and best-performing white blood cell
classification methods. It was found that in recent decades researchers have
been using ML and DL for white blood cell classification, but there are still
some challenges. 1) Availability of the dataset is the main challenge, and it
could be resolved using data augmentation techniques. 2) Medical training of
researchers is recommended to help them understand the structure of white blood
cells and select appropriate classification models. 3) Advanced DL networks
such as Generative Adversarial Networks, R-CNN, Fast R-CNN, and faster R-CNN
can also be used in future techniques.Comment: 23 page
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