AN APPROACH FOR AUTOMATIC SORTING OF TABLETS AND CAPSULES

Transcription

1 AN APPROACH FOR AUTOMATIC SORTING OF TABLETS AND CAPSULES Veska Georgieva Abstract: This paper presents an effective approach for sorting of pharmaceutic objects such as tablets and capsules according to different parameters such as color, shape and size, using image processing techniques. The color sorting is realized by color segmentation based on K-mean clustering. The shape and size of the tablets are identified by feature extraction based on Hough transform. In the paper are given also some results obtained by computer simulation of the proposed algorithm, applied on real images. Key words: automatic sorting, image processing, color segmentation, Hough transform. 1. Introduction Computer vision provides innovative solutions in the direction of industrial automation. In many situations, it provides effective solutions to menial tasks. Moreover, the repetitive tasks with more accuracy and sensitivity can be completed using automated instruments. Normally, sorting of the objects is done by manually. Old sorting method uses a set of inductive, capacitive and optical sensors do differentiate object color in the testing station [1]. No vision capability exists in the system to improve its performance and flexibility. In this case, there is a possibility of minor error which will affect the accuracy in sorting. Automated systems can be used to remove such human errors and also it saves time and money. In Fig.1 is shown the simplified diagram of an automated visual sorting system [2]. Fig. 1 Diagram of an automated visual sorting system Image processing is basically enhancing the images, which are taken using cameras considered as vision sensors for various applications. Nowadays different techniques have been developed for detecting the objects using vision systems. Color 327

2 histograms were first proposed by Swain and Ballard [3] as a first approach for object recognition. Other features such as orientation, gradient magnitude were added to the histograms by Schiele and Crowley [4]. These inventions helped in changing the rotation, direction and deformation but did not help in object recognition. In [5] is presented a system for mechatronic color sorting system solution with the application of image processing. The presented algorithm can detect the circular industrial objects, based on calculation of their centroids, in an image captured in real time by a web camera fixed at the end-effector. The algorithm is designed to detect the ranges of red, green and blue color. This paper presents another effective approach for sorting of pharmaceutic objects such as tablets and capsules according to color, shape, size and position, which is based on image processing techniques. The remainder of this paper is organized as follows: section 2 describes the basic stages in the proposed algorithm; section 3 presents the experimental results to show the effectiveness of the method and; section 4 presents our conclusion and some future works. 2. Basic stages of the algorithm The proposed algorithm includes pre-processing stage for noise reduction and image enhancement, and processing stage for sorting. Color sorting is realized by color segmentation based on K-mean clustering. The circular and elliptic shapes, size and position of the tablets are identified by feature extraction based on Hough transform. Pre-processing stage includes noise reduction and contrast enhancement. The block diagram of the proposed algorithm is shown in Fig.2. Fig. 2 Block Diagram of pre-processing stage As first is proposed to use a median filter in order to reduce some kind of noise, introduced by the creating of digital images from a camera. This method is a specific case of order-statistic filtering, in that the value of an output pixel is determined by the median of the neighbourhood pixels. The median is much less sensitive than the mean to extreme values (outliers). Median filter is therefore better able to remove the outliers without reducing the sharpness of the image and with less blurring of edges. The intensity of the image can be adjusted as next. So can be increased the contrast in image in order to obtain better visualization on the boundaries of the objects, which sometimes are obscured by the shadow. It is made on the base of calculation the histogram of the image and determination the adjustment limits. 328

3 Two basic methods for segmentation are proposed to be applied in regard to obtain more accuracy by sorting of the tablets. By sorting the tablets in colors is used a K-mean clustering segmentation. So we can specify the number of clusters to be partitioned and a distance metric such as Euclidian distance to quantify how close two objects are to each other. As result we can obtain images which correspond to different colors of the investigated objects. The flowchart of the proposed algorithm for sorting of tablets in colors is presented in Fig.3. Fig. 3 Flowchart of color segmentation for color sorting of the tablets As next are processed the created cluster-images as result of color segmentation in regard to identify the shapes, size and position of the tablets. It is made by feature extraction based on Hough transform (HT). Canny Operator is used for obtaining contours of the tablets and capsules. Then the Circular Hough Transform (CHT) is applied for detecting circles of known radius as well to detect circles of various radii. 329

4 This method is based on creating an accumulator matrix of size of the original image to be processed. The accumulator space is three-dimensional (for three unknown parameters x 0, y 0 and r) and defines a locus of points (x, y) centered on an origin (x 0, y 0 ) with radius r. Points corresponding to x 0, y 0 and r, which has more votes, are considered to be a circle with center (x 0, y 0 ) and radius r. An ellipse is defined by five parameters and it requires a 5-dimensional parameter space. It is used the Randomized Hough Transform (RHT), which randomly selects pixels from an image and fits them to a parameterized curve [6]. Because only a small random subset of pixels is selected, this method reduces the storage requirements and computational time needed to detect curves in an image. 3. Experimental results For the experiments were used real digital RGB images (N=10) with size 225x225. The original images have been done in jpeg file format. By pre-processing they are converted in bmp format. The formulated stages of image processing are implemented by computer simulation in MATLAB 8.1 environment. In Fig. 4a is presented an original image with 5 circle tablets in white, brown and red color with different sizes and 2 capsules in blue color with elliptic shape. Fig. 4b shows its enhancement modification after pre-processing stage. The created images after color segmentation are shown in Fig.5. a) b) Fig. 4 RGB image: a) original; b) its modification after pre-processing stage a) b) c) d) Fig. 5 Created images after color segmentation based on K-mean clustering Fig. 6 illustrates the results obtaining for sorting of tablets by shape and size. In Fig. 6b and Fig.6c are given the detected white tablets with their R max and R min respectively. Fig.6d presents the one of the blue overlapping capsules, which is with 330

5 the same size. The other is detected by the next following processing. In Table 1 are given the obtained results for the radii and coordinates of the center in detected tablets and the coordinates of the centroids and the angles of orientation to the X-axis by elliptic capsules. The results are evaluated by calculating the measures precision, recall and F-measure [7]. a) b) c) d) Fig. 6 Detected tablets and capsules Table 1 Experimental results for detected tablets and capsules Detected tablets X coordinate Y coordinate Radius R White with Rmax White with Rmin Red with with Rmax Red with with Rmin Brown Detected capsules X-coordinate of Y- coordinate of Angle of orientation to the centroid the centroid the X - axis Capsule Capsule Conclusion The paper presents a new and effective approach for sorting of pharmaceutic objects such as tablets and capsules according to different parameters such as color, shape and size, based on image enhancement in a pre-processing stage and combination of color segmentation and feature extraction on HT. The implemented studying and obtained experimental results has shown that: Detection and sorting of the pharmaceutical objects such as tablets and capsules is accurately with an average precision about 92.89%; The execution time for image processing is about 30 sec and depends from the size of the images and the number of the objects; The proposed approach can be very useful in other fields of the industrial engineering for sorting of different industrial objects. The implemented algorithm provides a basis for further investigations in more precise detection in cases of: Sorting of two or more tablets and capsules with the same color, which are overlapping; The background and the objects are from the same color. 331

6 332 XХIV MНТК АДП-2015 References: 1. Gavade G., Kharat P., Laga S., Cost effective approach for object sorting, International Journal of Computer Applications,Vol.52 N.16, pp.1-5, Priese L., Balthasar D., Erdmann T., Pellenz J., Rehrmann V., Zeppen J., Real-time detection of arbitrary objects in alternating industrial environments, Norwegian Society for Image Processing and Pattern Recognition pp , Swain, M., Ballard, D. Color indexing. International Journal of Computer Vision, Schiele, B., and Crowley, J. L. Recognition without correspondence using multidimensional receptive field histograms, International Journal of Computer Vision 36, pp , Atef. A., Sohair F., El-Shenawy A., Diab M., Design and development of 5-DOF color sorting manipulator for industrial applications, International Journal of Mechanical, Aerospace, Industrial and Mechatronics Engineering Vol:7, No:12,pp , Lei Xu, Erkki O., and Kultanena P., A new curve detection method Randomized Hough transform, Pattern Recognition Letters (11), pp , Faustino G., Gattass M., P. Carvalho, S. Rehen and C. de Lucena, Automatic embryonic stem cells detection and counting in fluorescence microscopy images, Monografias em Ciência da Computação, ISSN , No. 04, ЕДИН ПОДХОД ЗА АВТОМАТИЗИРАНО СОРТИРАНЕ НА ТАБЛЕТКИ И КАПСУЛИ Веска Георгиева Резюме: Статията представя ефективен подход за сортиране на фармацевтични обекти като таблетки и капсули в зависимост от различни параметри като цвят, форма и размери, като за тази цел са използвани различни методи за обработка на изображения. Сортирането по цвят е реализирано чрез сегментация по цвят на основата на групиране чрез осредняване. Формата и размерите на таблетките се идентифицират чрез извличане на признаци, базирани на трансформацията на Hough. В статията са представени също някои резултати, получени чрез компютърна симулация на предложения алгоритъм, приложен при работа с реални изображения. Data author: Veska Georgieva, Assoc. Professor, PhD, Department RCVT, Faculty of Telecommunications, TU of Sofia, Bulgaria, Sofia 1000, 8-Kliment-Ochridski Blvd., tel.: , е-mail: vesg@tu-sofia.bg