Different Methods of Image Segmentation in the

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Different Methods of Image Segmentation in the Process of Meat Marbling Evaluation Ludwiczak A.1, Ślósarz P.1, Lisiak D.3, Przybylak A.2, Boniecki P.2, Stanisz M.1, Koszela K.2, Zaborowicz M.2, Przybył K.2, Wojcieszak D.2, Janczak D.2, Bykowska M.1 1

Poznan University of Life Sciences, Faculty of Animal Breeding and Biology, Poland Poznan University of Life Sciences, Faculty of Agronomy and Bioengineering, Poland 3 Institute of Agricultural and Food Biotechnology, Division of Fat and Meat Technology, Poland 2

ABSTRACT The level of marbling in meat assessment based on digital images is very popular, as computer vision tools are becoming more and more advanced. However considering muscle cross sections as the data source for marbling level evaluation, there are still a few problems to cope with. There is a need for an accurate method which would facilitate this evaluation procedure and increase its accuracy. The presented research was conducted in order to compare the effect of different image segmentation tools considering their usefulness in meat marbling evaluation on the muscle anatomical cross – sections. However this study is considered to be an initial trial in the presented field of research and an introduction to ultrasonic images processing and analysis. Keywords: Meat marbling, image analysis, edge detectors, evaluation.

1. INTRODUCTION The level of marbling in meat is pointed as one of the primary traits strongly influencing meat quality and therefore deciding about meat eating quality [1]. In order to facilitate the marbling level evaluation and decrease its subjectivity, different computer vision methods are analysed and tested. However marbling assessment based on digital images usually must come over some difficulties like low – contrast images and blurred images. From among different computer tools used for marbling level assessment one of the most promising seem to be the neural networks and pattern recognition techniques used broadly for different biological applications [2-11]. Reliability and objectivity of the NNs is based on their capability to learn and solve problems, on the basis of learning datasets [12-13]. As a consequence, it NNs are capable to generate an adaptive neural network model to distinguish the edge detection from marbling on muscle images [14]. Marbling particles are generally easily detected by segmentation tools used for feature detection [15]. Finding a proper tool which would work for marbling pattern detection remains a key issue to be dealt with. Fortunately, advanced methods used in computer vision are available. From the most simple segmentation methods like thresholding, to more advanced edge detectors The presented research was conducted as a consequence of many difficulties connected with the marbling images segmentation, like the case of non – uniform brightness of evaluated structures and no clear boundary between muscle and IMF. The aim is to select the best segmentation tools from among the presented ones in order to apply them on ultrasonic muscle images in the future. In general the process of image segmentation is one of the most important parts of the image analysis. The aim of this process is to separate the objects of interest from the image background. According to this assumption, the segmentation in the presented study should isolate the marble particles from the muscle tissue.

2. MATERIALS AND METHODS The number of 40 digital images used in the presented study was obtained by scanning anatomical muscle cross – sections in water immersion in order to prevent the light reflexes. All the image analysis and processing procedures were made by means of ImageJ software [http://imagej.nih.gov/ij/]. The images were firstly evaluated by implementing the semiautomatic thresholding, with the threshold value established individually and subjectively for each image by a trained personnel. As a consequence binary images were obtained Fig. 1. The percentage of marbling (% IMF) and number of its particles was calculated for the selected region of interest (ROI) on the muscle cross section. Images assessed this way were divided into two groups depending on the marbling level: images with highly marbled muscle and muscles with low level of marbling.

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These results of marbling level evaluation were compared with the effects of selected edge detectors implementation, namely the Canny [16], Deriche [17-18] and Hessian [19-21] edge detectors. These edge detectors are a very popular group of tools used in computer vision for image segmentation, capable to isolate only the structures necessary for further processing, leading to reduction of the amount of data. All the selected algorithms application was preceded with the setting of uniform image scale, the region of interest (ROI) selection and image conversion to gray – scale. The extracted marbling was assessed by means of tools responsible for particle analysis, like the number of objects per ROI, their percent in the ROI and shape descriptors like circularity and solidity. In general each one of the selected edge detectors puts the image through similar processing steps, which may be explained on the basic of the most popular edge operator – the Canny capable of edge detection due to gradient magnitude computation and hysteresis thresholding Fig. 2. The Canny edge detector consists of a few procedures, starting with image smoothing with the Gaussian Filter in order to reduce noise and camera artifacts. Secondly the image gradients are determined by applying the Sobel operator both horizontally and vertically. Next, the non – maximum suppression is applied in order to remove pixels, which were not classified as edges. The final step is the hysteresis based on two thresholds, upper and lower one. All pixels with a gradient higher than the upper threshold are classified as edges. Finally the image is displayed. The other used edge detectors, the Hessian filter and the Deriche edge detector Fig.2 work similarly to the Canny. Since the edges are always image regions with strong intensity, they are detected by convolving the image with an appropriate kernel, while the problem of noise is solved by smoothing tools implementation. All the differences between edge operators are connected with different convolution kernels and different smoothing methods (Gaussian blurr, the infinite impulse response filter). The Pearson Correlation coefficients for the image analysis results was made using the SAS 9.4 software.

Figure 1. Binary image of the pig loin anatomical cross – section thresholding.

3. RESULTS AND DISCUSSION The image edge detection proceeded as shown below and was preceded by the ROI selection. The choose of proper ROI is a very important part of image analysis, as the selected area should be free from image distorts and representative [22]. The active thresholding was connected with selection of a proper threshold, therefore it was highly dependent from the experience of the observer, and consequently subjective. However the simple thresholding may give a very good results in case if the segmentation methods requiring manual operations are necessary [23]. The green background was used in order to increase the contrast between the image background and the analyzed object [24-26].



Figure 2. Canny, Hessian and Deriche edge operators, and ROI selection.

As the active thresholding can be compared to the visual marbling assessment, the obtained binary images were considered as equal to the visual marbling grading. Consequently, the correlations between the marbling assessment on the thresholded images (binary images) and the marbling level detected by the edge detectors, are considered as indicators of these tools usability for marbling level assessment. These correlation coefficients were non – significant (P > 0.05), (Table 1 ,2, 3). Table 1. The Pearson correlation coefficients between the number of objects/edges detected by the selected edge operators. Segmentation tool Hessian Canny Deriche Binary

0,231

0,403

Deriche

0,150

0,283

Canny

0,668*

0,443

* significant, P < 0.0001 However some moderate correlations were found between the results of the binary image analysis and the results of Canny (0,403) and Deriche detector application (0,443). A highly significant and strong correlation was found between the results of the Canny and Hessian filter (0,668), which may result from the similar edge detection procedures implemented in these tools. In order to make a more precise analysis of the usefulness of the analyzed segmentation tools, the authors decided to investigate a different design of this study, and divide the data set on two groups differing with the marbling level. The threshold was set on the arithmetic mean of marbling in the investigated muscle samples, which reached 1,9 % meat marbling level. All the observations above this threshold were classified as high marbling level. Consequently, the observations below this threshold were classified as low marbling level. Table 2. The Pearson correlation coefficients between the number of objects/edges detected by the selected edge operators in the images of muscles with a high marbling level.

Segmentation tool Hessian Canny Deriche Binary

0,516

0,299

0,368



Table 3. The Pearson correlation coefficients between the number of objects/edges detected by the selected edge operators in the images of muscles with a low marbling level.

Segmentation tool Hessian Canny Deriche Binary 0,301 0,178 0,515 Some moderate correlations were found between the results of binary image analysis and the results of Hessian (0,516) and Deriche detector application (0,368) for images presenting a high marbling level Table 2. Moderate correlation coefficients were also found between the results of binary image analysis and the results of Hessian (0,301) and Deriche detector application (0,515) for images of muscles characterized with a low marbling level Table 3. In the case of marbling investigation in two data sets (low and high marbling in meat), the Hessian is shown to be of higher significance compared to the one data set assessment (no division against marble degree). The Canny operator usefulness also changes with the change of the experiment design. The correlation Canny - Binary was found to be moderate considering the one data set, and low, considering two data sets. The low Pearson Correlation coefficients for the Canny and Hessian edge detectors based on the Gaussian smoothing, might be a result of incorrectly adjusted Gaussian kernel, which blurred out the edges making them hard to detect [27]. The Gaussian kernel implementation is one of the Canny operator stages, leading to smoothing (blurring the image). This smoothing filter is commonly used to effectively reduce the level of noise in the image, preserving the edges and boundaries at the same time [28]. The edge preservation capability decides about the Gaussian Blur superiority over the other smoothing methods. According to the literature the Hessian and Canny edge detectors’ parameters have to be perfectly matched to detect edges without error. The non - significant correlations between the analyzed feature detectors may also be a result of a low number of observations or the detectors’ parameter settings, besides the previously mentioned Gaussian smoothing operator. According to the Open Computer Vision Library [http://opencv.org/], each computer vision algorithm has a few adjustable parameters which define its accuracy and the computation time. These parameters set too high will detect not only marble particles but also the image distorts or the muscle structure, but set too low, they will miss important information, like unclear marbling. The difference between the results of Hessian and Deriche applied on images of low marbled and high marbled meat, may be considered as a confirmation of the previously mentioned conclusion. Therefore to assure the a good localization of edges, understood as a minimum distance between the found edge pixel and the actual edge, an attempt has to be made in order to adapt the algorithm to the specificity of the analyzed material. Opposite to Canny edge operator, the Hessian and Deriche edge detectors seemed to protect unclear edges, corners and junctions, . In future analysis segmentation tools sensitive to image brightness and colour will be used, as more suitable methods for meat marbling evaluation [29-30].

4. CONCLUSIONS Out of different edge detection methods, this paper discussed the results of the Canny, Deriche and Hessian filters. The feature extractors used in computer vision gave different results considering the meat marbling extraction on the presented muscle scans. The calculated Pearson Correlation coefficients indicate a moderate usefulness of the presented tools for marbling assessment. The used tools may have extracted too much edges connected with the muscle structure, but not marbling itself, or they skipped some unclear edges. This result can be considered as an indicator to change the parameter settings and/or change the method of the image preprocessing for edge detection in the future image analysis. Moreover, the presented detectors should be applied on a larger number of images. ACKNOWLEDGEMENT The study was supported by Grant no. N N312 212136 from the Polish Ministry of Science and Higher Education.

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