First, image enhancement will be included to increase the efficiency of the algorithm by performing some adjustment and filtering techniques to increase the visibility of the image. Then to extract features by calculating the average of each feature such as area, volume, major axis length and minor axis length for each image. Finally, it will be sorted from the average of the properties into a sample after sorting the values of the average properties of each sample itself, which was done in the classification phase.
I would like to take this opportunity to express my gratitude to the following persons who are supporting and helping me to complete my Final Year Project successfully. Not to forget all the examiners who help me a lot to improve my project through my mistakes. Last but not the least, I owe a lot to the lab technicians, lab supervisors, friends and those who had helped directly or indirectly throughout the project completion period.
Currently, indirect immunofluorescence (IIF) is the proposed solution used to detect an ANA. Nowadays, automatic cell detection of images obtained from antinuclear antibodies (ANA) has become a great demand due to the subjective result, time consuming and poor quality and not standardization result. Classification of ANA in this paper is based on the pattern classification in human epithelial type 2 (HEp- 2) images.
Due to the limitations of other research, many major problems have already been identified, for example the lack of quantitative data provided to physicians, the variety of analysis systems and optics, making results variable and subjective.
Objective
Referring to the previous method that has been performed in classifying the pattern of antinuclear antibody in HEp-2, there is a need to have a medical expertise to support a decision. Current implementation requires at least two experts for positivity interpretation will lead to a conflicting opinion between the experts. Another thing, if the IIF test is done manually, it will produce a subjective result and lead to poor quality and not standardize results.
This is done by applying MATLAB algorithm to the images to distinguish the cell images in which pattern. Commercial image and database will get it from the hospital or can be taken from the internet, which is Mivia Hep-2 Images Dataset. After that, some adjustment and filtering techniques were applied to improve the images.
Then there will be a comparative analysis on the most suitable clustering technique that is for segmenting images. After that, pattern classification will participate in the programming that extracts the features from the images and then adds some algorithm to classify the images using MATLAB and image processing toolbox.
Relevancy of Project
This project consists of two phases, the first is mostly emphasis in literature review, find out the basic concept and principle of the project. The second phase of the project is focused on making a systematic and appropriate procedure to perform the analysis and experiment more efficiently. In addition, the image enhancement method, which is by using RGB color space model to obtain clearer images and increase the efficiency of the algorithm, is applied in the experiment.
The analysis will continue on the second part of the stages that extract and classify features from HEp-2 images using intensity order pool and bag of words. Based on the previous research, the method depends on specialists to observe HEp-2 slides through the fluorescence microscope, which suffers from a number of shortcomings, such as being subjective and labor-intensive.[4]. The most important step to increase the efficiency of algorithm for segmentation by choosing a suitable color space model also yields a significant value.
RGB color information consists of red (R), green (G) and blue (B), while color saturation (H) and (S) are for HSV. After performing the analysis, it shows that the new composite algorithm which is adaptive fuzzy moving k-means is not much affected by the first step of grouping value and also noise. To overcome this problem, a new modification of FCM, which is by using adaptive spatial, is designed by Yu et al.
Hobson [9] presented a benchmark platform of classification anti-nuclear antibody Hep-2 image by applying CAD system which is not only simple but also effective. This CAD system is inspired from the recent success research in the object classification domain represented by Cell Bank. Regarding local information extraction, local data from the Hep-2 images were segmented using well-known scale-invariant feature transform (SIFT) descriptor.
Although over 98% accuracy was achieved to discriminate the centromere and cytoplasm with the other models, the performance of the system dropped to 64.2% when all 6 models were considered. Donut-shaped merging regions are used for resizing when collecting spatial decomposition histogram input.[11] The system achieved about 83.53% accuracy on the test set. The combination of ROI, texture and HOG using SVM classifier gives the best performance based on experimental results.
Experimental Work .1 Propose Method
Tools and Software
Design Approach
For the proposed algorithm, 24-bit RGB is needed and each component has an 8-bit depth. Basically, by using Euclidean distance, all the data can be assigned to the nearest center. For the group that has the smallest fitness number is represented as 𝐶𝑠, and the largest fitness number is 𝐶𝑙.
By measuring the angle of J with respect to 𝑢𝑖𝑗, the degree of membership of 𝑥𝑗 in the ith group can be identified. By measuring the angle of J with respect to 𝑣𝑖, the cluster center of 𝑣𝑖 can be identified, i:1.C. When there is a difference in terms of membership values below a certain threshold, the convergence of this algorithm can be achieved.
In the MATLAB algorithm, there are four features that are extracted from each of the images which are area, perimeter, major axis length and minor axis length. By calculating the mean value and grouping for each feature of the training dataset, the images can be classified into several cell patterns which are homogeneous, centromere, nuclear, fine and coarse punctate. Based on [20], the corresponding points can be correctly matched using the approximation error to be less than 20°.
For classification, the cell image is classified and regrouped based on the training dataset properties already obtained in the feature extraction section. Using the learned dictionary, this can be represented by the encoding of all blocks when the test image is divided into a number of small blocks. Below is the process flow for the research project, in order to complete the objectives.
The procedures for the methodology of this project have not been finalized yet, therefore not much can be considered and explained. For the first part of this project, image enhancement will take part as the initial step to classify the cell image. Then, the feature extraction author will extract value data from the image using shape feature extraction.
Finally, for the classification, the method used is data clustering based on the analysis of shape properties. To enhance the image, the image will go through a process that converts it to the grayscale image and applies some adjustments and filters to get clearer images. It is hoped that the research can be achieved when the procurement of tools and materials required for analysis proceeds smoothly.
Further research can be done by applying the best feature extraction and classification technique in order to achieve high accuracy value such as Intensity Order Pooling and Bags of Words.
