Computational Intelligence-Based Cell Nuclei Segmentation from Pap Smear Images

2016 ◽  
pp. 262-284
Author(s):  
Savitha Balakrishnan ◽  
Subashini Parthasarathy ◽  
Krishnaveni Marimuthu
Keyword(s):  
Image Processing ◽  
Cervical Cancer ◽  
Computational Intelligence ◽  
Pap Smear ◽  
Correct Diagnosis ◽  
Optimization Techniques ◽  
Cell Nuclei ◽  
Swarm Optimization ◽  
Nuclei Segmentation ◽  
Segmentation Problem

Automated Segmentation of cell nuclei in Pap smear images plays an important role in the cervical cancer cell analysis systems to make a correct diagnosis decision. The aim of this chapter is to detail about the variety of computational intelligence and image processing approaches developed and used for the nuclei segmentation. In additional, the threshold based segmentation problem is treated as an optimization problem with an objective of preserving both the size and volume of the cell nuclei and also to segment the nuclei region from the original microscopic Pap smear image with the help of Particle Swarm Optimization (PSO) and Ant Colony Optimization techniques (ACO). Experimental results are shown, compared in quantitative and qualitative manner as well as the main advantages and limitations of each algorithm are explained.

Computational Intelligence-Based Cell Nuclei Segmentation from Pap Smear Images

Biometrics ◽  
2017 ◽  
pp. 259-280
Author(s):  
Savitha Balakrishnan ◽  
Subashini Parthasarathy ◽  
Krishnaveni Marimuthu
Keyword(s):  
Image Processing ◽  
Cervical Cancer ◽  
Computational Intelligence ◽  
Pap Smear ◽  
Correct Diagnosis ◽  
Optimization Techniques ◽  
Cell Nuclei ◽  
Swarm Optimization ◽  
Nuclei Segmentation ◽  
Segmentation Problem

Automated Segmentation of cell nuclei in Pap smear images plays an important role in the cervical cancer cell analysis systems to make a correct diagnosis decision. The aim of this chapter is to detail about the variety of computational intelligence and image processing approaches developed and used for the nuclei segmentation. In additional, the threshold based segmentation problem is treated as an optimization problem with an objective of preserving both the size and volume of the cell nuclei and also to segment the nuclei region from the original microscopic Pap smear image with the help of Particle Swarm Optimization (PSO) and Ant Colony Optimization techniques (ACO). Experimental results are shown, compared in quantitative and qualitative manner as well as the main advantages and limitations of each algorithm are explained.

Automated Cell Nuclei Segmentation on Cervical Smear Images Using Structure Analysis

2021 ◽  
Vol 51 ◽  
pp. 105-115
Author(s):  
Wan Azani Mustafa ◽  
Low Zhe Wei ◽  
Khairul Shakir Ab Rahman
Keyword(s):  
Cervical Cancer ◽  
Pap Smear ◽  
Standard Technique ◽  
Successful Implementation ◽  
Cell Nuclei ◽  
Structural Element ◽  
Detection Techniques ◽  
Nuclei Segmentation ◽  
Common Cancer ◽  
Early Century

Cervical cancer is a common cancer that affects women around the world, and it is also the most common cancer in the developing countries. The cancer burden has increased due to several factors, such as population growth and ageing. In the early century, the systematization of cervical cancer cells takes some time to process manually, and the result that comes out is also inaccurate. This article presents a new nucleus segmentation on pap smear cell images based on structured analysis or morphological approach. Morphology is a broad set of image processing operations that process images based on shape, size and structure. This operation applies a structural element of the image to create an output image of the same size. The most basic of these operations are dilation and erosion. The results of the numerical analysis indicate that the proposed method achieved about 94.38% (sensitivity), 82.56% (specificity) and 93% (accuracy). Also, the resulting performance was compared to a few existing techniques such as Bradley Method, Nick Method and Sauvola Method. The results presented here may facilitate improvements in the detection method of the pap smear cell image to resolve the time-consuming issue and support better system performance to prevent low precision result of the Human Papilloma Virus (HPV) stages. The main impact of this paper is will help the doctor to identify the patient disease based on Pap smear analysis such as cervical cancer and increase the percentages of accuracy compared to the conventional method. Successful implementation of the nucleus detection techniques on Pap smear image can become a standard technique for the diagnosis of various microbiological infections such as Malaria and Tuberculosis.

scholarly journals A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification

2021 ◽  
Vol 11 (9) ◽  
pp. 4091
Author(s):  
Débora N. Diniz ◽  
Mariana T. Rezende ◽  
Andrea G. C. Bianchi ◽  
Claudia M. Carneiro ◽  
Daniela M. Ushizima ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Pap Smear ◽  
Hierarchical Classification ◽  
Cell Nuclei ◽  
Test Analysis ◽  
Pap Smear Test ◽  
Microscopy Image ◽  
Feature Based ◽  
Cervical Cells ◽  
Number Of Classes

Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells; accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visually by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods.

scholarly journals Computer-Assisted Screening for Cervical Cancer Using Digital Image Processing of Pap Smear Images

2020 ◽  
Vol 10 (5) ◽  
pp. 1800 ◽  
Author(s):  
Kyi Pyar Win ◽  
Yuttana Kitjaidure ◽  
Kazuhiko Hamamoto ◽  
Thet Myo Aung
Keyword(s):  
Image Processing ◽  
Cervical Cancer ◽  
Cancer Screening ◽  
Cervical Cancer Screening ◽  
Digital Image Processing ◽  
Digital Image ◽  
Pap Smear ◽  
Support Vector ◽  
Computer Assisted ◽  
Screening System

Cervical cancer can be prevented by having regular screenings to find any precancers and treat them. The Pap test looks for any abnormal or precancerous changes in the cells on the cervix. However, the manual screening of Pap smear in the microscope is subjective with poorly reproducible criteria. Therefore, the aim of this study was to develop a computer-assisted screening system for cervical cancer using digital image processing of Pap smear images. The analysis of Pap smear image is important in the cervical cancer screening system. There were four basic steps in our cervical cancer screening system. In cell segmentation, nuclei were detected using a shape-based iterative method, and the overlapping cytoplasm was separated using a marker-control watershed approach. In the features extraction step, three important features were extracted from the regions of segmented nuclei and cytoplasm. RF (random forest) algorithm was used as a feature selection method. In the classification stage, bagging ensemble classifier, which combined the results of five classifiers—LD (linear discriminant), SVM (support vector machine), KNN (k-nearest neighbor), boosted trees, and bagged trees—was applied. SIPaKMeD and Herlev datasets were used to prove the effectiveness of our proposed system. According to the experimental results, 98.27% accuracy in two-class classification and 94.09% accuracy in five-class classification was achieved using the SIPaKMeD dataset. When the results were compared with five classifiers, our proposed method was significantly better in two-class and five-class problems.

A Novel Nucleus Detection on Pap Smear Image Using Mathematical Morphology Approach

2021 ◽  
Vol 49 ◽  
pp. 53-61
Author(s):  
Nadzirah Nahrawi ◽  
Wan Azani Mustafa ◽  
Siti Nurul Aqmariah Mohd Kanafiah ◽  
Wan Khairunizam Wan Ahmad ◽  
Mohamad Nur Khairul Hafizi Rohani ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Mathematical Morphology ◽  
Pap Smear ◽  
Image Quality Assessment ◽  
Median Filter ◽  
Correct Diagnosis ◽  
Conventional Technique ◽  
Papanicolaou Test ◽  
Pap Smear Test ◽  
Contrast Stretching

The fourth most common form of cancer among women is cervical cancer with 569,847new cases and 311,365 reported deaths worldwide in 2018. Cervical cancer is classified as the third leading cause of cancer among women in Malaysia, with approximately 1,682 new cervical cases and about 944 deaths occurred in 2018. Cervical cancer can be detected early by cervical cancer screening. Papanicolaou test, also known as Pap smear test is conducted to detect cancer or precancer in the cervix. The disadvantage of this conventional method is that the sample of microscopic images will risk blurring effects, noise, shadow, lighting and artefact problems. The diagnostic microscopic observation performed by a microbiologist is normally time-consuming and may produce inaccurate results even by experienced hands. Thus, correct diagnosis information is essential to assist physicians to analyze the condition of the patients. In this study, an automatedsegmentation system is proposed to be used as it is more accurate and faster compared to the conventional technique. Using the proposed method in this paper, the image was enhanced by applying a median filter and Partial Contrast Stretching. A segmentation method based on mathematical morphology was performed to segment the nucleus in the Pap smear images. Image Quality Assessment (IQA) which measures the accuracy, sensitivity and specificity were used to prove the effectiveness of the proposed method. The results of the numerical simulation indicate that the proposed method shows a higher percentage of accuracy and specificity with 93.66% and 95.54% respectively compared to Otsu, Niblack and Wolf methods. As a conclusion, the percentage of sensitivity is slightly lower, with 89.20% compared to Otsu and Wolf methods. The results presented here may facilitate improvements in the detection performance in comparison to the existing methods.

scholarly journals Current Status of Computational Intelligence Applications in Dermatological Clinical Practice

2020 ◽  
Vol 14 (1) ◽  
pp. 6-13
Author(s):  
Carmen Rodríguez-Cerdeira ◽  
José Luís González-Cespón ◽  
Roberto Arenas
Keyword(s):  
Image Processing ◽  
Candida Albicans ◽  
Computational Intelligence ◽  
Candida Species ◽  
Correct Diagnosis ◽  
Optical Microscope ◽  
The Other ◽  
Current Status ◽  
Geometric Descriptors ◽  
Yeast Fungus

Background: The yeast infections are increasingly frequent and the correct diagnosis consists of the identification of the yeast fungus, which in our case we are going to refer to the different species of Candida. The prescription of a broad-spectrum antifungal without taking into account the etiological agent, leads to an increase in the resistance to these treatments. Objective: The objective of this work is to differentiate Candida albicans from other Candida species (Candida spp.) By means of digital images obtained from the optical microscope. Material and Methods: It has reviewed about 100 photographs from patients in our consultations. In this study we will use the microscopic images of the Candida variety to be processed later with the Octave programming language and its image processing package (image-2.8.0). Results and Discussion: This system is able to differentiate Candida albicans from the other varieties of Candida such as C. parapsilosis, C. krusei, and C. kefyr with accuracy. The candida identifier application, which was designed and programmed in Octave, allows identification of candida species by locating certain geometric descriptors, such as the centroid and the surfaces of circular objects within the images. The program was highly effective for the diagnosis of Candida spp. So, we got a sensitivity and specificity above 90% with the images used. Conclusion: The results that we obtain from the Candida spp. identifier system that opens the way to be able to work with images obtained from the optical microscope.

Automatic cell nuclei segmentation and classification of cervical Pap smear images

2019 ◽  
Vol 48 ◽  
pp. 93-103 ◽  
Author(s):  
Pin Wang ◽  
Lirui Wang ◽  
Yongming Li ◽  
Qi Song ◽  
Shanshan Lv ◽  
...  
Keyword(s):  
Pap Smear ◽  
Cell Nuclei ◽  
Nuclei Segmentation
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