Breast cancer is one of the major threats to the human being. Early identification can prevent some of the premature deaths. Manual methods are sometimes very tedious and time consuming. Moreover manual diagnosis can be prone to error. Automated analysis can reduce the overhead of the manual diagnosis and reduce the error. In this work, an automated computer assisted framework has been proposed to analyze and detect the type of the disease from the current condition of the breast. Histological slides have been used for automated diagnosis. SIFT based feature selection and extraction method has been used followed by a Bag-of-Features method. The extracted features are classified by a metaheuristic supported Artificial Neural Network. Three models have been compared in terms of accuracy and obtained results are reported in a comprehensive manner. © 2018 IEEE.

SOUMYA SEN

GENERAL

S CHAKRABORTY

S CHATTERJEE

A CHATTERJEE

K MALI

S GOSWAMI

University of Calcutta (informally known as Calcutta University or CU) is a collegiate public state university located in Kolkata, West Bengal, India.&nbsp;Within India it is recognized as a &quot;Five-Star University&quot; and accredited &quot;A&quot; Grade by National Assessment and Accreditation Council.

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The University of Calcutta has secured the Fifth position among the Universities of India in the prestigious &quot;Indian University Ranking 2019&quot; list, released by the NIRF of the Ministry of Human Resource Development, Government of India.

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It was established on 24 January 1857, and was one of the first institutions in Asia to be established as a multidisciplinary and Western-style university. Its alumni and faculty include several heads of state, heads of government, social reformers, prominent artists, only academy award winner and Dirac medal winner in India, many Fellows of the Royal Society and five Nobel laureates as of 2019 which is highest in South Asia.

University Of Calcutta

Automated Breast Cancer Identification by analyzing Histology Slides using Metaheuristic Supported Supervised Classification coupled with Bag-of-Features

Proceedings - 2018 4th IEEE International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2018

Image segmentation is one of the major research domains in several applications including retinal blood vessel segmentation, which is an active research area. Vasculature structure analysis is an interesting and effective method for disease detection and analysis. In this work, a gradient-based blood vessel segmentation technique is proposed to assist retinal image analysis and to extract the retinal vessels. Edge detection is considered one of the major steps in the present work to characterize the boundaries. Itis used to reduce the unusual information and to preserve the necessary structural information. Various filters are constructed to gradient computation and edge detection. In the current work, a new method along with a new filter (kernel) has been proposed to detect edges efficiently. The results are compared with some well-known kernels. The proposed approach achieved Pratt Score 99.1536 value, which outperformed the corresponding values obtained by the other standard edge detectors, namely Sobel, Prewitt, Canny, and Robert's. © 2017 IEEE.

2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics, UPCON 2017

Gradient approximation in retinal blood vessel segmentation

Microscopic image analysis is one of the challenging tasks due to the presence of weak correlation and different segments of interest that may lead to ambiguity. It is also valuable in foremost meadows of technology and medicine. Identification and counting of cells play a vital role in features extraction to diagnose particular diseases precisely. Different segments should be identified accurately in order to identify and to count cells in a microscope image. Consequently, in the current work, a novel method for cell segmentation and identification has been proposed that incorporated marking cells. Thus, a novel method based on cuckoo search after pre-processing step is employed. The method is developed and evaluated on light microscope images of rats’ hippocampus which used as a sample for the brain cells. The proposed method can be applied on the color images directly. The proposed approach incorporates the McCulloch's method for lévy flight production in cuckoo search (CS) algorithm. Several objective functions, namely Otsu's method, Kapur entropy and Tsallis entropy are used for segmentation. In the cuckoo search process, the Otsu's between class variance, Kapur's entropy and Tsallis entropy are employed as the objective functions to be optimized. Experimental results are validated by different metrics, namely the peak signal to noise ratio (PSNR), mean square error, feature similarity index and CPU running time for all the test cases. The experimental results established that the Kapur's entropy segmentation method based on the modified CS required the least computational time compared to Otsu's between-class variance segmentation method and the Tsallis entropy segmentation method. Nevertheless, Tsallis entropy method with optimized multi-threshold levels achieved superior performance compared to the other two segmentation methods in terms of the PSNR. © 2017 Wiley Periodicals, Inc.

Microscopy Research and Technique

Modified cuckoo search algorithm in microscopic image segmentation of hippocampus

This work describes an method for biomedical image enhancement using modified Cuckoo Search Algorithm with some Morphological Operation. In recent years, various digital image processing techniques are developed. Computer Vision, machine interfaces, manufacturing industry, data compression for storage, vehicle tracking and many more are some of the domains of digital image processing application. In most of the cases, digital biomedical images contains various types of noise, artifacts etc. and are not useful for direct applications. Before using it in any process, the input image has to be gone through some preprocessing stages; such preprocessing is generally called as image enhancement. In this work, a new technique has been proposed to enhance biomedical images using modified cuckoo search algorithm and morphological operation. Presence of noise and other unwanted objects generates distortion in an image and it will affect the ultimate result of the process. In case of biomedical images, accuracy of the results is very important. It may also decrease the discernibility of many features inside the images. It can affect the classification accuracy. In this work, this issue has been targeted and improved by obtaining better contrast value after converting the color image into grayscale image. The basic property of the cuckoo search algorithm is that the amplitudes of its components are capable to objectively describe the contribution of the gray levels to the formation of image information for the best contrast value of a digital image. The proposed method modified the conventional cuckoo search method by employing the McCulloch's method for levy flight generation. After computing the best contrast value, morphological operation has been applied. In morphological operation based phase, the intensity parameters are tuned for quality enhancement. Experimental results illustrate the effectiveness of this work. © 2017 IEEE.

2017 8th Industrial Automation and Electromechanical Engineering Conference, IEMECON 2017

Biomedical image enhancement based on modified Cuckoo Search and morphology

Automated, efficient and accurate classification of skin diseases using digital images of skin is very important for bio-medical image analysis. Various techniques have already been developed by many researchers. In this work, a technique based on meta-heuristic supported artificial neural network has been proposed to classify images. Here 3 common skin diseases have been considered namely angioma, basal cell carcinoma and lentigo simplex. Images have been obtained from International Skin Imaging Collaboration (ISIC) dataset. A popular multi objective optimization method called Non-dominated Sorting Genetic Algorithm - II is employed to train the ANN (NNNSGA-II). Different feature have been extracted to train the classifier. A comparison has been made with the proposed model and two other popular meta-heuristic based classifier namely NN-PSO (ANN trained with Particle Swarm Optimization) and NN-GA (ANN trained with Genetic algorithm). The results have been evaluated using various performances measuring metrics such as accuracy, precision, recall and F-measure. Experimental results clearly show the superiority of the proposed NN-NSGA-II model with different features. © 2017 IEEE.

Detection of skin disease using metaheuristic supported artificial neural networks

Object recognition can be done based on local feature description algorithm or through global feature description algorithm. Both types of these descriptors have the efficiency in recognizing an object quickly and accurately. The proposed work judges their performance in different circumstances such as rotational effect scaling effect, illumination effect and blurring effect. Authors also investigate the speed of each algorithm in different situations. The experimental result shows that each one has some advantages as well as some drawbacks. SIFT (Scale Invariant Feature Transformation) and SURF (Speeded Up Robust Features) performs relatively better under scale and rotation change. MSER (Maximally stable extremal regions) performs better under scale change, MinEigen in affine change and illumination change while FAST (Feature from Accelerated segment test) and SURF consume less time. © 2017 by IGI Global. All rights reserved.

Fulltext

Feature Detectors and Motion Detection in Video Processing

Analysis of different feature description algorithm in object recognition

Automated breast cancer identification by analyzing histology slides using metaheuristic supported supervised classification coupled with bag-of-features

Journal	Data powered by TypesetProceedings - 2018 4th IEEE International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2018
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open Access	No