Swaroopa H N^*, Basavaraj N Jagadale, Omar Abdullah Murshed Farhan Alnaggar, Vijayalakshmi Hegde and Abhisheka T E

Department of PG studies and research in Electronics, Kuvempu University, Shankaraghatta, Shimoga, India

Corresponding Author E-mail: swaroopampl@gmail.com

DOI : https://dx.doi.org/10.13005/bpj/2872

Abstract

The most demanding aspect of digital image processing is segmenting an image efficiently. Cell segmentation or classifying cells in an image is essential while analyzing cell images in medical research, especially in spot diagnosis, cancer cell detection, and live-cell imaging segmentation forms a crucial component. This research examines existing segmentation algorithms and suggests a new segmentation technique that employs image filtering and thresholding. Thresholding is an essential part of image analysis and segmentation. Finally, the segmented image and the FCM (fuzzy C-means) based clustered image are merged. In terms of accuracy, sensitivity, dice-coefficient, and Jaccard-coefficient, the simulation coupled with ground truth data is proven to produce better segmentation outcomes.

Keywords

Antinuclear antibody (ANA); Gaussian filter; Human epithelial type2 (HEp-2) cell image; Image fusion; Immune fluorescent (IIF); Segmentation

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Copy the following to cite this article: Swaroopa H. N, Jagadale B. N, Alnaggar O. A. M. F, Hegde V, Abhisheka T. E. Human Epithelial Cell Image Analysis and Segmentation using Threshold Based Fusion Technique. Biomed Pharmacol J 2024;17(1).

Copy the following to cite this URL: Swaroopa H. N, Jagadale B. N, Alnaggar O. A. M. F, Hegde V, Abhisheka T. E. Human Epithelial Cell Image Analysis and Segmentation using Threshold Based Fusion Technique. Biomed Pharmacol J 2024;17(1). Available from: https://bit.ly/42BsdCb

Introduction

The rapid development of new technologies in the field of image processing has helped in discovering human abnormalities at an early stage and is faster than manual methods. Image analysis and segmentation have an essential role in the medical field and help in the early detection of diseases in critical health conditions. Due to the structural complexity and type of the cell image, radiologists may face difficulties in extracting the necessary features of the image, ¹ which are crucial in treating the patient. Therefore, correct and meaningful segmentation is a challenging task and is required for further process.

The most used substrate for finding the antinuclear antibody (ANA) is the human epithelial type2 (HEp-2) cell samples, which are a natural protein array with plenty of antigens. The discovery of ANA in human serum is a vital transmission instrument for connective tissue syndromes, and immune fluorescent (IIF) is the reference method for ANA testing. Indirect immune fluorescent (IIF-ANA) test is becoming increasingly crucial for analyzing the cell image because of its ease and inexpensiveness ^{1, 2}. The attributes of epithelial type-2 (HEp-2) cells are an oval shape with merged properties of connected tissues, which help in microscopic observation and provide better result in detecting the number of antibodies with better resolution. Therefore, cell image segmentation is an essential task that examines the appropriate biological features such as cell type, shape, group, etc. The experimental studies are focused mainly on (HEp-2) cell image and aim to identify and segment the cells based on ground truth data.

Several computer vision methods have been developed for a variety of applications in image processing. The first phase of computer vision includes pre-processing and image segmentation.³ In this stage dissimilar objects are identified and separated from the background. The next part is feature extraction, where object are measured. The computing of image features to quantitatively evaluate some significant features of object. These clusters are combined to create a feature vector during feature mining. ^{4, 5} The next level is classification. At this point, the output is only a decision to conclude the class of each object. The structural study is an explanation of images to properly recognize and judge the vital details of images. ^{6 – 15}

Thresholding

Thresholding is the fundamental way to do image segmentation. The grayscale image is taken as input data, and the output image will be in the form of binary. The simplest thresholding ³ method substitutes every pixel in an image with a dark pixel if the image intensity {\display style I {i, j}} Ii, j is less than definite static constant T (that is, Ii, j < T {\display style I {i, j} < T}), or a bright pixel if the image intensity is more than that constant.

Global threshold

The histogram is a graphical way of representing the image intensity. It reveals two points for an indication from the background and the foreground, respectively. Manual thresholding ³ involves intensity value (threshold) such that total pixels having an intensity value lesser than the threshold fit to one segment, and the rest of the part fits the other. Global thresholding provides better results only when the degree of intensity separation between the two points in the image. It is an unsophisticated segmentation selection ⁴.

Figure 1: Graphical representation of histogram data. Click here to view Figure

Otsu’s threshold

Image segmentation splits an image into its subareas and is a crucial task in mining image features, especially in medical image analysis and segmentation approaches. Due to its understanding of perception and ease, the thresholding technique is important and essential part of diagnosing medical images. ^{5, 6} Usually, the histogram-dependent threshold choice technique, and “the maximum between-class variance method proposed by the Japanese scholar Otsu used”. The selection of principle threshold criteria technique reflects the largest interclass difference between the object and background. Similarly, by varying the Otsu’s threshold level is either 0 or 1 to get an FCM threshold. Thus, in the analysis of general as well as medical images, this technique provides better results compared to some of the popular methods. ^{11, 12} The algorithm steps are given by

Initial estimation of T

Segmentation using of T

g1 pixel brighter than T

g2 pixel darker than T

[computation of average Intensities m1 and m2 of g1 and g2]

if [T-Tnew]>∆Tbacktostep 2, otherwise stop.

Adaptive threshold

In an adaptive threshold, the threshold value is calculated for a particular region, thus it varies from the threshold rate at every pixel position based on the adjacent pixel values. ⁶ To estimate the threshold T (x, y) i.e. the threshold value at pixel position (x, y) in the image.^13-16

Multilevel threshold

Multilevel thresholding is the most applicable and popular image segmentation technique. ⁷ Here, thresholding is assigned to segment a gray-level image into some discrete areas. Here, more than one threshold criterion is chosen to analyze the given image. ¹⁰

Optimal threshold

Choosing a threshold in the valley between two overlapping peaks classifies the pixels inappropriate way. The selection of optimal threshold and the optimal regulation can be done inevitably by a similar learning process and is explicitly for the optimal selection of threshold used in predications. The directions from examples and counterexamples have been projected by reducing an entropy function. ⁸ The rules have the procedure of disjunctions of conjunctions of predicates. Thus, such a method can study commands from cases, and depends on an optimality principle. ^{9, 10}

pb (z), p0 (z), prob. distributions of background, object pixels.

μb, μ0 : the means of the distributions.

σb, σ0 : the standard deviations of the distributions.

Pb, Po : the a-priori probabilities of background, object pixel. ¹⁷

Figure 2: Optimal threshold selection in between the overlapping peaks. Click here to view Figure

K-means clustering

This algorithm is a simple and effective unsupervised technique, which can be used for various applications like abnormality detection, pattern analysis, image scene understanding, etc. It classifies a sample set Y (y1, y2, y3…. y_n) into groups with the endeavor at minimizing an objective function J is given by, ¹⁸

where, n represents the quantity of samples and c_i is the cluster center, where, ‖y_j⁽ⁱ⁾ -c_i ‖² is the “Euclidean distance measure between a data point y_j and the cluster center c_i”. ¹⁹

Algorithm steps

Compare the distributed pixel intensity of the objects.

Initialize the centroids with K random intensities.

Recurrence the step till each cluster get labeled.

Here, N is the sample number of the i^th cluster.

Cluster the points based on distance of their intensities from the centroid intensities.

Compute the new centroid for each of the clusters. ²⁰

Fuzzy c-means clustering

FCM algorithm for detecting optimal partition of cluster points is one of the popular techniques for analysis of both cell and MRI images. It splits the data points into subareas named cluster groups. Here the clustering points are automatically assigned after the pre-processing stage, and the associate function is the curve that defines how each point in the input space is mapped to the degree of associated value between 0 and 1. During the iteration process, at each cycle, optimized cluster points are generated. ^{18, 21, 22}

Where, A represents the image and A = {Z (A, B), 0 < A ≤ M, 0 < B ≤ N}, and M*N denotes the cluster points.

where, Y= {Y0, Y1, Y2,…. Yc-1} signifies the data points

X = [μc (p, q)] is the matrix,

‘n’ represents the constant.

μ_c (p, q) is the associated data points of Z (A, B) at the C^th cluster,

“d_c (p, q) is the Euclidian distance of the data point Z (A, B) from the C^th cluster”. ²¹

Clusters number K (2 ≤ K < M*N) with original clustering matrix values are labeled.

The data points Y_c, and fuzzy matrix X_c (p, q) are fixed by utilizing equations [9], [10], correspondingly.

The procedure is recurring till the quantities varying among two iterations and is not more than ε. ^{22, 23}

The rest of this paper is structured as, proposed work in Section 2, the flowchart, and algorithm of the proposed method are presented in section 3, the result are given in section 4, and the conclusions in section 5.

Proposed method

Firstly, the input cell image is transformed to grey scale, and a Gaussian filter is employed to eliminate noise contamination in the cell image. ⁸ A denoised image is then thresholded by an automated higher threshold value in conjunction with binary smoother. However, the thresholded pixel values are fewer than the image’s mean frequency. The resulting image is processed with a “remove border” operation, and the thresholded image is equalized using an adaptive histogram. The picture is simultaneously denoised and segmented using an FCM (fuzzy C-means) based clustering approach. Finally, combining these two with the fusion procedure results in a new segmented image. The resultant image is more suitable to ground truth data, and is compared with existing thresholding algorithms. ¹²

Gaussian filter

The Gaussian filter is a type of linear smoothing filter in which the weights are chosen according to the Gaussian function procedure for removing noise from a normal distribution. Two-dimensional discrete Gaussian zero mean-function is employed as a smoothing filter in image processing.

Higher Threshold

Bernsen’s locally adaptive binarization approach is utilized to extract the adhesive cell image feature from a grayscale picture. It is also tested for various neighborhood values and contrast limitations.

(Z_low) is the lowest and (Z_high) is the highest gray level pixel value in a square r x r neighborhood centered at pixel (x, y),

If the contrast measure C (x, y) < l, the neighborhood is made up of only one class, foreground or background. Furthermore, r and l values altered based on the image regions employed.

Binary smoother

Binary smoothing is a method for smoothing data or filtering noisy data. The data is first categorized, and the sorted values are distributed across many segments or cells. The technique conducts local smoothing since they refer to a neighborhood of data. Smoothing by binary means replaces each value in a binary with the binary mean value.

Adaptive histogram

The image is splits into discrete blocks, and a histogram is computed for each segment. Thus, adaptive histogram equalization computes many histograms, each of which corresponds to a different part of the picture. Consequently, it improves the local contrast and edge definitions in all segregated sections of the image.

Material and methods

Flow chart

Figure 3: Schematic flowchart of the proposed method. Click here to view Figure

Algorithm steps

Input: Cell image to be segmented

Output: Segmented image

A cell image is converted to a gray-scale image.

Image denoising is performed with the Gaussian filter to diminish the noise contamination in the cell image.

Higher_threshold value is used to extract the required features from a denoised image, with binary smoother.

Segmented image is processed by adaptive histogram, which is more accurate and noise-free.

The combination of FCM clustered image along with the segmented image is fused to obtain the final target image.

Results and Discussions

Performance matrices

The proposed algorithm is coded and executed in (MATLAB R2020a). The cell image (Hep-2) and the corresponding reference data is extractedfrom SNPHep-2 Dataset. The results are compared with existing methods and are found to be better.

Accuracy

It specifies the perfect class of the image pixels. The measure of accuracy ²⁴ is given by

Pixels which are perfectly incorporated into the given class are represented by TP and TN represents the pixels which are not belonging to the specified class. FN is incorrectly projected pixels, which are not fitting to the specified class.

Sensitivity

Sensitivity is calculated through positive predicted and negative predicted values, which is the ratio of true outcome of all segmented results. ²⁴

Dice co-efficient

Dice-coefficient examines the identical things in between the segmented data and the reference data, which is retrieved from manual segmentation. Dice-coefficient of segmented image ^{24, 25} is given by

where, |M|and |N|cardinalities of two sets.

Jaccard-coefficient

Jaccard-coefficient is as same as the dice-coefficient, which calculates the commonness in-between ground truth data with different segmentation methods employed in this paper and ^{24, 25} is given by

Experimental results

Figure 4: a. Input cell raw image, b. Ground truth image, c. Enhanced image, d. Grayscale image Click here to view Figure

Figure 5: Segmented by: a. Global_ threshold, b. Otsu’s_threshold, c. Adaptive_threshold, d. Multilevel_threshold. Click here to view Figure

Figure 6: Segmented by: a. K-means_clustering, b. FCM_clustering, c. FCM_threshold, d. Higher_threshold, e. Proposed method. Click here to view Figure

Table 1: Comparison of Hep-2 cell image segmentation result.

Cell image	Global_T	Otsu’s_T	Adaptive_T	Multi- Level_T	Fcm-C	K-means	Fcm_T	Higher_T	Proposed method
Accuracy  in %	97.39	95.67	86.65	95.67	95.67	94.89	97.34	97.41	97.66
Sensitivity in %	92.01	85.82	85.82	85.82	85.70	91.14	91.42	90.60	95.68
Dice Coefficient in %	93.96	89.20	89.20	89.20	89.17	88.07	93.46	93.98	94.44
Jaccard Coefficient in %	88.67	80.50	80.50	80.50	80.45	78.68	87.20	87.93	89.47

 

Figure 7: Performance evaluation of suggested study with existing techniques. Click here to view Figure

Conclusion

A unique threshold-based image segmentation approach is suggested in this paper. The proposed model’s performance is compared to manual and current segmentation approaches, as well as the pre-processing procedure. However, instead of current segmentation strategies, the suggested method delivers superior segmentation by integrating the FCM-based clustering technique with image segmented by higher thresholding, which is one of the study’s important advantages. Furthermore, classic threshold-based segmentation approaches such as Otsu’s, multi-level, and FCM thresholding methods, suffer, loss of intrinsic information after segmenting the cells, whereas global and adaptive thresholding give superior segmentation, but the threshold criteria must be set manually. Although unsupervised clustering techniques such as K-means and FCM are popular, the findings of both approaches do not meet the acceptable level throughout the assessment, particularly for adhesive cell segmentation. As a result, the proposed method has an edge and is useful in segmenting the adhesive Hep-2 cell image without sacrificing essential information at the fusion level. While examining the biological aspects of the Hep-2 cell image, radiologists will find the entire research quite useful. The simulation output of the proposed study is more effective and efficient in terms of computing. It is discovered that performance metric’s sensitivity parameter improves by around 3%, and the segmented image’s effectiveness is assessed in terms of accuracy, sensitivity, dice-coefficient, and Jaccard-coefficient. The main contribution of this work is the improvement of performance measure, which is supported in all of these factors and is clear from the outcomes of the proposed algorithm.

Acknowledgement

Standard test image samples used in this work were extracted from SNPHEp-2 Dataset, (https://staff.itee.uq.edu.au/lovell/snphep2/).

Conflict of interest

There is no conflict of interest.

Funding Source

There are no funding sources

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