2 B-Ultrasonic room of People's Hospital of Gansu Province,. 730000 Lanzhou ... Keywords: Prostate Ultrasonic Image, Pulse Coupled Neural Network, Image.
Pathologic Region Detection Algorithm for Prostate Ultrasonic Image Based on PCNN Beidou Zhang1, Yide Ma1, Dongmei Lin1, and Liwen Zhang2 1
School of Information Science & Engineering, Lanzhou University, 730000 Lanzhou, China 2 B-Ultrasonic room of People’s Hospital of Gansu Province, 730000 Lanzhou, China
Abstract. It is quite important and difficult for doctors to detect pathologic regions of prostate ultrasonic images. An automated region detection algorithm is proposed to solve this problem, especially for ultrasonic images containing all kinds of noise and speckle. First, all the pixels of an ultrasonic image are fired by Pulse Coupled Neural Network (PCNN). Then after being processed by morphological closing, binary reversing and region labeling, the seeds are detected automatically using PCNN, by which the region of interest (ROI) of the ultrasonic image is detected by Region Growing. In the end, we code the ROI by pseudo-color. Detected pathologic regions can be used for further clinical inspection and quantitative analysis of ultrasonic images. Keywords: Prostate Ultrasonic Image, Pulse Coupled Neural Network, Image Segmentation, Pseudo-color.
1 Introduction In the field of modern clinical diagnosis, medical imaging technologies, such as US, CT, MRI, PET, have been playing an important role in detecting and treating of numerous diseases. The radiologists present 2D or 3D images, giving patients a detailed view of their anatomies. Because of the diverse physiological properties, tissues would display kinds of medical images by different medical imaging equipments. Appropriate equipments should be selected to detect different tissues because single medical imaging equipment is not suitable for all kinds of disease diagnosis. Ultrasonic imaging is a common modality in current medical practice. It is used to image soft tissues, such as lungs, prostate, liver, spleen, thyroid or the neonatal brain. The advantages of ultrasound imaging are its rapid speed, high security, cost effectiveness and portability of the equipment, which make it more suitable than CT or MRI in many situations [1]. In medical imaging, ultrasonic image analyzing remains a difficult task. And for the same image, the opinions of different doctors are not consistent [2]. Along with the improvement of image acquisitions, more and more image data are obtained from various imaging modalities. Especially for video stream, doctors need to process large numbers of data every day, and the manual or semiautomatic processing technologies can not satisfy F.P. Preparata and Q. Fang (Eds.): FAW 2007, LNCS 4613, pp. 244 – 251, 2007. © Springer-Verlag Berlin Heidelberg 2007
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their requirements. In this instance, Computer-Aided Diagnosis (CAD) technology provides doctors with automated and impersonal processing methods. However, most medical images have lots of shortcomings, such as complexity, variability or blur, requiring users to operate the CAD system manually sometimes [3~5]. PCNN is a new artificial neural network which comes from the research of small mammals’ visual properties [6]. It has an excellent ability for segmentation because of its synchronous pulse burst, changeable threshold and controllable parameters. Combining PCNN with mathematical morphology, we propose an automated pathologic region detection algorithm.
2 PCNN and Mathematical Morphology In 1990, Eckhorn proposed the model of Pulse-Coupled Neural Network after researching the synchronous pulse burst phenomenon of the cat visual cortex [7]. PCNN has predominance in image processing, image recognition, moving object recognition and so forth [8]. Fig.1 shows a single neural model of PCNN. It is composed of three elements: Dendritic Tree, Linking Modulation and Pulse Generator Element. The Dendritic tree includes two parts of the neuron element, the linking and the feeding. The linking region incorporates neighborhood information, namely other neurons’ outputs, with the internal activity of the neuron element. The feeding region incorporates the input signal information and also neighbor information. Dendritic Tree receives the inputs from other neurons, and then transmits them through two channels, one is F Channel and the other is L Channel. Lj is added to a constant positive bias and then multiplied by Fj which comes from F Channel. Pulse Generator Element is composed of a pulse generator and a comparator, whose threshold is changeable. It compares the internal activity with a dynamic threshold to decide whether the neuron fires or not. When the threshold θj is greater than Uj, the pulse generator is turned off and the pulses are stopped to put out. Otherwise, the pulse generator is opened, the neuron fires, and a
Threshold function
Linking input Y
W
L
Vș f(x)=1+ȕx
Weight F Feeding input Dendritic Tree
×
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ș 0 U
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Fig. 1. A neuron model of PCNN
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Step function Pulse Generator
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pulse or pulse sequence is emitted. The whole Pulse-Coupled Neural Networks work as follows: If the neuron has a pulse to put out, the changeable threshold would increase abruptly. Therefore, the second firing is forbidden, and the threshold decays exponentially. After that, θj would be less than Uj again, the neuron is activated secondly. Distinctly, these pulses are input to other neurons to affect their firing states. Every neuron carries their iterative computations as follows:
①
The Feeding and Linking compartments receive inputs from stimulus and previous states, and communicate with neighboring neurons through the synaptic weights M and W respectively. The values of these two compartments are determined by,
Fij [ n] = exp(−αF )Fij [ n −1] +VF ∑mijklYkl [ n −1] + Sij
②
Lij [ n] = exp(−α L ) Lij [ n −1] + VL ∑ wijklYkl [ n − 1]
(1) (2)
The states of these two compartments are combined in a second order fashion to create the internal state of the neuron, U. The combination is controlled by the linking strength, β. The internal activity is calculated by,
U ij [ n] = Fij [ n ] (1 + β Lij [ n ])
(3)
③ In pulse generate compartment, U is compared with dynamic threshold, θ, to
produce the output, Y. If Uij is greater than θij, the Pulse Generator would output 1, otherwise 0, by
④
Yij [ n] = 1 if Uij [ n] > θij [ n] or 0 otherwise
(4)
When the neuron fires, the dynamic threshold, θ, increases its value abruptly, and then decays until the neuron fires again by,
θij [ n] = exp(−αθ )θij [ n − 1] + Vθ Yij [n − 1]
(5)
When PCNN is used for image processing, it is a monolayer two-dimensional array of laterally linked neurons. The number of neurons in the network is equal to the number of pixels in the input image. One-to-one correspondence exists between image pixels and neurons. Each pixel is connected to a unique neuron and each neuron is connected with the surrounding neurons. The intensities of pixels are put into neurons correspondingly, and the neuron firing equals to the pixel firing. Mathematical Morphology has been enriched and developed continuously since it was put forward for the first time by G. Matheron and J. Serra in 1964. This subject is based on strict mathematical theories such as integral geometry, set algebra and topology theory, and refers to modern probability theory, neo-mathematics, etc. Though its theories foundation is very complex, the basic principle is relatively simple. When Mathematical Morphology is applied to image processing, the pixels are regarded as the sets of points, a structural element is used to do the operations as follows: intersection, union, and shift. Accordingly, other mathematical morphology processing algorithms come into being in terms of the basal set operations [9]. In practical image processing applications, dilation and erosion are in common use and can make up of opening, closing, hitting, thinning or thicking in various combinations. PCNN is the research result of biology, so it has the biological background and has an equivalence relation with mathematical morphology in image processing. A
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neuron’s firing in some time would bring on the neurons besides it firing and these neurons would also bring on their surrounding neurons’ firing. The pulses would spread like automatic waves. Inverse the fired regions, we will get the shrunk regions which are caused by spreading pulse. So, the parallel pulse spreading is equal to the dilation operation in mathematical morphology. Using this property, we can construct other mathematical morphology operators.
3 Ultrasonic Image Processing Using PCNN Ultrasonic images are usually characterized by complexity, low contrast and all kinds of speckle, making ultrasonic image processing very difficult. PCNN was introduced into the field of ultrasonic image processing, and edge detection algorithm and image enhancement algorithm were proposed by us for the first time. We use PCNN to segment the original ultrasonic image and one area of the image is obtained after each iteration. Detect this area’s edge, judge whether the current pixel is an edge pixel and mark it in another matrix. Also mark the current area, and place it to another matrix. After several segmentation and region detection, all pixels are detected. Combine the results, and we can get the image’s edges. PCNN model is the simulation of visual behavior, and its output reflects some human visual properties. All of the parameters of PCNN, threshold θij is one of the most active parameters. It determines neurons’ firing time. After neurons’ firing, θij increases to a great value, and then decays exponentially. When Uij is greater than θij again, the neuron fires once more. Now θij is greater than the corresponding pixel’s gray level, and has been stretched exponentially. Here, we set Y as equation (6):
Yij [ n] = θij if Uij [ n] ≥ θij [ n] or 0 otherwise
(6)
Superpose the firing images of different time, and we get the primary enhanced image. Fig. 2 and Fig. 3 depict the processing outcome of edge detection algorithm and image enhancement algorithm using PCNN.
(a) Gallbladder
(b) Sobel Edge Detector (c) Canny Edge Detector
(d) Our algorithm
Fig. 2. Gallbladder ultrasonic image edge detection
4 Pathologic Region Detection Algorithm Aiming at the more and more medical data and noise of ultrasonic images, we propose a pathologic region automatic detection algorithm based on PCNN. Prostate ultrasonic images are selected as experiment images, which contain small sick regions
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(a) Gall-stone image
(b) Histogram equalization
(c) Reference [10] method
(d) PCNN enhancement
Fig. 3. Gall-stone ultrasonic image enhancement
depicted in Fig. 4. The concrete algorithm is depicted as follows, in which the preprocessing part contains taking out irrespective regions, mean filtering and smoothing. (1) Acquire the original ultrasonic image I, and preprocess it; (2) Segment I by simplified PCNN: a. Initial PCNN’s parameters:
，α =0.3，V =240,
β=0.4
θ
θ
⎡ 0.5 1 0.5⎤ W1 = ⎢⎢ 1 0 1 ⎥⎥ , ⎢⎣ 0.5 1 0.5⎥⎦
Fig. 4. Prostate ultrasonic image
define threshold matrix T, storing matrix Y for fired pixels, storing matrix Y1 for pixels fired the first time, edge storing matrix E, fired regions storing matrix Z; b. T(i,j)=Vθ, n=1;
，
，
；
c. Scan the image matrix I, and calculate them one by one, F(i,j)=S(i,j) L=W1*Y[n-1] U=F(1+β*L)
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If U(i,j)> T(i,j) Y(i,j)=1, isolate the (i,j) pixel so as to ensure it will never fire again; else Y(i,j)=0
；
d. Detect the edge of the region fired this time, and note it in E; label the region, note it in Z;
；
e. All the neurons unfired are decayed by multiplying exp(-αθ) f. n=n+1, if all neurons are fired, end calculation, output E and Z, else go back to step c. (3) Do closing operation for Y1; (4) Inverse the outcome of step (3), and label it; (5) Take the pixels of small region as seed pixels, use region growing to extract ROI; (6) Code the ROI using pseudo-color to enhance it; (7) Integrate the outcomes, and output them.
(a) Original images
(b) Detected ROI
(c) Output Images
(d) Manual Results
Fig. 5. Detection results by the automated region detection algorithm
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We test the algorithm under the platform of MATLAB 7.0. Fig.5(b) are extracted outcomes detected by our algorithm. From Fig.5, we can see that the extracted ROI have good region integrality, uniformity and connectivity, which make the segmentation much more robust and exact. Using pseudo-color to enhance the ROI, we get the result shown in Fig.5(c), and Fig.5(d) are manual results. Hereto, the image details have been enhanced according to their intensities, by which quantitive analysis can be done further. The disadvantage of Region Growing is that the operator needs to select seed pixels manually. In order to extract useful regions, operators must select a seed among them. In this paper, the algorithm of seed pixels automatic extraction based on PCNN successfully solves these problem of selecting seed pixels though ultrasonic images are badly degraded.
5 Conclusion The disadvantage of Region Growing is that operator needs to select seed pixels manually. In order to extract useful regions, operators must select a seed among them. In this paper, an algorithm of seed pixels automatic extraction based on PCNN is proposed, which solves the problem of selecting seed pixels though ultrasonic images are badly degraded due to much noise. This is the successful application of PCNN for ultrasonic image detection. Medical images are characterized by complexity and variety, so the automatic detection algorithms for all kinds of medical images need to be researched further. Acknowledgments. The authors thank Dr. Aboul Ella Hassanien for providing the original ultrasonic images and his help for us. This work was supported by National Natural Science Fund of China (NO.60572011) and 985 Special Study Project (LZ85231-582627).
References 1 Jensen, J.A.: Medical ultrasound imaging. Biophysics and Molecular Biology, 1–13 (2006) 2 Stippel, G., Philips, W., Govaert, P.: A tissue-specific adaptive texture for medical ultrasound images. Ultrasound in Med. & Biol. 1211–1223 (2005) 3 Van Stralen, Marijn, Bosch, Johan, G., et al.: A semi-automatic endocardial border detection method for 4D ultrasound data. In: Medical Image Computing and ComputerAssisted Intervention, 7th International Conference, Proceedings, pp. 43–50 (2004) 4 Levienaise-Obadia, Barbara, Gee, Andrew: Adaptive segmentation of ultrasound images. Image and Vision Computing, 583–588 (1999) 5 Hiransakolwong, Nualsawat: Automated liver detection in ultrasound images. In: 4th International Conference on Image and Video Retrieval, pp. 619–628 (2005) 6 Johnson, J.L., Padgett, M.L.: PCNN Models and Applications. IEEE Trans. Neural Networks, 480–498 (1999) 7 Eckhorn, R., Reitboechk, H.J., Arndt, M., et al.: Feature linking via correlated synchronization among distributed assemblies: Simulation of results form cat cortex. Neural Compu. 293–307 (1990)
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8 Lindblad, T., Kinser, J.M.: Image Processing using Pulse-Coupled Neural Networks. Springer, Heidelberg (2005) 9 Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley Longman Publishing Co., Redwood City, CA, USA (1992) 10 Meihong, S., Junying, Z., Yonggang, L., et al.: A New Method of Low Contrast Image Enhancement. Application Research of Computers, 235–238 (2005)
