- Open Access
Two-step verification of brain tumor segmentation using watershed-matching algorithm
© The Author(s) 2018
- Received: 17 August 2016
- Accepted: 20 July 2018
- Published: 14 August 2018
The Correction to this article has been published in Brain Informatics 2018 5:11
Though the modern medical imaging research is advancing at a booming rate, it is still a very challenging task to detect brain tumor perfectly. Medical imaging unlike other imaging system has highest penalty for a minimal error. So, the detection of tumor should be accurate to minimize the error. Past researchers used biopsy to detect the tumor tissue from the other soft tissues in the brain which is time-consuming and may have errors. We outlined a two-stage verification-based tumor segmentation that makes the detection more accurate. We segmented the tumor area from the MR image and then used another algorithm to match the segmented portion with the ground truth image. We named this new algorithm as watershed-matching algorithm. The most promising part of our model is the status checking of the tumor by finding the area of the tumor. Our proposed model works better than other state-of-the art works on BRATS 2017 dataset.
- Brain tumor segmentation
- Median filter
- Magnetic resonance imaging
- SIFT algorithm
- Status checking
- Watershed-matching algorithm
Segmentation is the fundamental step in medical image analysis. Though past researchers have prepared their research, still now it is a vast research field because of the variation of the data of MRI. The authors in  use watershed segmentation and EM–GM algorithm for segmenting brain tumor. But they did not mention any potential dataset. Similar type research was approached by the authors in  who use support vector machine classifier to classify the tumor from the normal tissue. But, due to the fragile training set and not a better technique of feature extraction, their algorithm cannot robustly classify tumor. Some authors had tried scale-invariant feature transform (SIFT) algorithm to find the feature points and match the tumor region. As this algorithm finds the feature points in the high-cluster region, sometimes they misclassify the normal tissue as high-cluster tumor tissue . Besbes et al.  introduced a model together with discrete Markov random field (MRF) for the segmentation of brain tumor. But the parameter estimation and computing probability for this method are very difficult. Shen et al.  introduced traditional fuzzy C-means (FCM) clustering algorithm. But it is prone to noise that may affect the pixel intensities and may have improper segmentation. Chen et al.  applied K-mean clustering and knowledge-based algorithm for biomedical image segmentation. But it only takes into consideration the image intensity, thereby not producing adequate outputs in noisy images. Many efforts have explored artificial neural network (ANN) . Edge-based segmentation techniques cannot work well due to having inherent speckle noise and texture characteristics.
In addition, K-nearest neighbor (KNN) , support vector machine (SVM) , Bayesian algorithm, hidden Markov model, conditional random field , high-dimensional features with level set  are different segmentation algorithms. Unsupervised algorithms start to evolve in recent days [12, 13], albeit they are still in the early stages with non-autonomous.
We propose a model that has two levels of authentication system to detect tumor. We named it as the WM (watershed-matching) algorithm. For segmenting the tumor region, we used the classical watershed algorithm and then use SIFT (scale-invariant feature transform) algorithm for matching the segmented region with the original image. For calculating the volume of the tumor, we used a very different technique this time. We had developed an algorithm that will use the help of Freesurfer software to find the cortical thickness, and then, we will use this model to build a volume of the tumor and then can differentiate between the benign and malignant type.
3.1 Image database
We used BRATS 2012 dataset that has multicontrast MR scans of 30 glioma patients, out of which 20 have been acquired from high-grade (anaplastic astrocytomas and glioblastoma multiform tumors) and 10 from low-grade (histological diagnosis: astrocytomas or oligoastrocytomas) glioma patients that had been manually annotated with two ground truth tumor labels (edema and core) by a trained human expert. The training data also contained simulated images for 25 high-grade and 25 low-grade glioma subjects with the same two ground truth labels. For each patient, multimodal (T1, T2, FLAIR and post-gadolinium T1c) MR images are available. I will use the BRATS 2013 test set that has multicontrast 10 high grades. The Leaderboard set has 11 + 10 high-grade glioma patients. It has also multimodal T1-weighted, T2-weighted, T2/FLAIR and post-gadolinium T1-weighted MR images. We have collected the tumor database from the MICCAI 2012 Challenge on Multimodal Brain Tumor Segmentation.
3.2 Noise removal of input image
In this stage, we used the modified version of the bilateral filter for noise removal. Bilateral filter works very good for smoothening the steplike edge features. But the main problem with this filtering is that it only works well when the gradient changes are not very high. High-gradient changes have some outliers, and the window cannot detect those outliers. The modified version of this bilateral filtering is the trilateral filter that has tilted window to track the high-gradient regions. It works as the same way as the bilateral filtering works. It finds the gradient changes, but the difference is that it finds the skewed gradient. It is an extension to the bilateral filter . Images corrupted with impulse noise can be removed by trilateral filter . We used this filter for de-noising mixed noise and for image restoration. This filter smoothens the edges of the image and remains the details of the images fixed. It considers the nearby pixel information with the help of very narrow spatial window and needs a few iteration processes than bilateral filtering. It reduces the standard deviation and variance from the original image.
3.3 Segmentation method
3.3.1 Watershed segmentation algorithm (WSA)
To understand the watershed algorithm, we can think of a grayscale image as geological landscape as a metaphor where the watershed means the dam that divides the area by river system.
Let consider the algorithm: A hole is punched at each regional local minimum, and the entire topology is flooded from below by letting the water rise through the holes at a uniform rate. Pixels below the water level at a given time are marked as flooded. When the water level rises, the flooded region will also grow. When this occurs, the algorithm will construct a one-pixel-thick dam that separated the two regions. The flooding continues until the entire image is segmented into separate catchment basins divided by the ridge lines.
Algorithm starts with setting an initial threshold, Ti. Morphological operation is performed for thresholding the intensity level globally. This operation begins with setting the structuring element that works like filter. These elements will filter out the background intensities in the image. The morphological operation is done to remove the dark and bright spot in the image and it is performed by using opening operation first which is like the erosion operation and then the closing operation likely to the dilation operation. When we are done with the morphological operation, we then computed the filtering operation which was done by convolving the morphologically smoothed image with a gaussian 3 × 3 kernel. Once this filtering was done, the gradient image operation was performed by finding the x and y gradient of the image and then found the gradient magnitude. The magnitude is the square root of the squared summation of the x and y gradient. Then, we selected a threshold for that gradient image. The threshold was set by computing the histogram operation of the maximum intensity value of the convolution operation of the morphological smoothed image.
3.4 Scale-invariant features transform (SIFT)
When we were done by segmenting the tumor region from the original image, we then performed the SIFT operation to match the segmented image by watershed with the ground truth image that is given in our benchmark dataset BRATS 2012. We computed the features from the two images, and to do that, we needed to perform the keypoints between the two images and then find the keypoint descriptor around that keypoints. We have shown the steps of this method in Fig. 2. Now, we will put down the steps in detail that we have performed for matching.
3.4.1 Scale-space peak finding
3.4.2 Keypoints detection
The pixel of the input is compared with the neighborhood pixels of above and below and thus found the local maxima and minima of the DoG (difference of Gaussian).
3.4.3 Keypoints localization
3.4.4 Keypoint orientations
The orientation to each keypoint provides rotation invariance. The more invariance, the better it is. The magnitude and orientation are calculated for all pixels around the keypoints, and a histogram is created where 360 degrees of orientation are broken into 36 bins and each bin is proportional to the magnitude of gradient at that point.
3.4.5 Descriptor computation
The gradient magnitudes and orientations are sampled around the keypoint location. Then, these sampled values are illustrated with small arrows at each sample location .
We have introduced the overall steps of SIFT algorithm to create a new era in the image segmentation accuracy-check process. We implement a 16 × 16 array, and an 8-bin histogram is used for computing the keypoints orientation.
4.1 2D results
To initiate the watershed transform algorithm, input image needs to be filtrated by applying filter to improve the different factors of the image so that the algorithm can be compiled elucidately
Original image variance
Original standard deviation
Filtered standard deviation
In the same way, closing operation is performed where the main image is firstly dilated. After this opening–closing operation, the original image is rendered, and thus, we get the segmented region as in the fourth row.
4.2 3D results
We analyze our algorithm for 3D MR images. We construct 3D image using 3D slicer, and we apply watershed algorithm. The input image is loaded in the software; then, we can see the directional images in three different windows. The volume model is built with the pre-chosen color. The first model is corpus callosum labeled color: green. The second model is frontal lobe white matter right labeled 17 (threshold 17:17), color: green. The third model is frontal lobe white matter left labeled 17 (threshold 17:17), color: green. Once we have segmented the target structure, we will use the Model Maker module to generate a surface volume, available in the full dropdown menu on the top toolbar. We also have a series of parameters within this section that we can modify, according to different parameters such as relief, color and luminosity. Once they are configured, three-dimensional image can be generated by clicking “Create New Model Hierarchy” option. To better visualize the results, we can exclude the lower windows of multiplanar representations or change their distribution for a better correlation .
These tools are not exclusively circumscribed to the medical diagnosis field; they are also used in education, which in recent years has been changing traditional teaching methods for applications and technology that facilitate the interaction of students with the contents [21–24].
4.3 Sift results
4.4 Tumor area
The tumor area is calculated to check the status of the tumor. As in our case, the area calculated was 269 for this MR image. So, it was in the initial stage. If the detected white pixels ≥ 500, then it will be in critical stage. To check that whether this pixel value calculation was correct or not, we computed the thickness calculation using the Freesurfer software and compared the result with our result. Though the result was not 100% correct, it gives the corrected result for most of the MRI test images.
The classical watershed algorithm segments the region perfectly, but for further clarification, we performed the SIFT algorithm to match the segmented tumor with the ground truth. So, our proposed algorithm provides two-step verification result.
Brain tumor is treatable if it has been identified in the earliest stages of the disease. In this paper, we proposed and developed a novel approach for brain tumor segmentation and detection. Our main contribution consisted of modeling improved watershed algorithm with three steps of de-noising filtering and designing scale-invariant feature transform algorithm where the optimized features were selected. Traditional over segmentation problem could be minimized by our improved algorithm as the MR images are highly affected by noise and artifacts. We preprocessed the images using artifacts removal, median filter and trilateral filter for improving the segmentation quality. Due to this improved combination, our proposed method is far better than any single or other combination algorithms. To check the accuracy of our algorithm, we compared the result with the truth images and acquired 98.5% accuracy. Here, we also introduced status checking of the tumor. We calculated the area of the tumor and then set a decision rule to decide whether it is in a critical or initial stage. This status checking made our system more robust. Our framework can be used in the general application.
In future, we would use it not only for brain tumor segmentation but also for other applications like the bone tumor, lung tumor or other segmentation purposes. We will reduce several manual interactions. This will help the physicians to prosecute the further treatment process in advance to treat tumor patients.
SMKH wrote the whole manuscript and he had also written the code in MATLAB. MA assisted on giving ideas and directions. All authors read and approved the final manuscript.
S. M. Kamrul Hasan is currently working as a PhD Graduate Research Assistant at Rochester Institute of Technology (RIT), NY, USA, at the Center for Imaging Science since fall 2017. He is working as a researcher at “Biomedical Modeling, Visualization and Image-guided Navigation Laboratory” where he is working on augmented reality for medical visualization. He received his BSc in Electrical & Electronics Engineering. He worked as a lecturer in the Department of Computer Science and Engineering at Daffodil International University, Bangladesh, from 2016. His research interests are on the area of biomedical imaging, image processing, machine learning, computer vision, deep neural network. He had published research papers in SPRINGER, IEEE, and ACM. He is a member of the IEEE Computer Society and the Association for Computing Machinery (ACM).
Mohiuddin Ahmad received his BS degree in EEE from Chittagong University of Engineering and Technology (CUET), Bangladesh, and his MS degree in Electronics and Information Science (EIS) from Kyoto Institute of Technology of Japan in 1994 and 2001, respectively. He received his PhD degree in CSE from Korea University, Republic of Korea, in 2008. He is working as a professor in the Department of EEE at KUET. Moreover, Dr. Ahmad had been serving as the Head of the Department of Biomedical Engineering from October 2009 to September 2012. Prof. Ahmad served as the Head of the Department of Electrical and Electronic Engineering from September 2012 to August 2014. From July 2014, Prof. Ahmad has been serving as the Sub-Project Manager (SPM) of the UGC, HEQEP, Sub-Project, CP#3472, titled on Postgraduate Research in BME. His research interests include biomedical signal and image processing, computer vision and pattern recognition, human motion analysis, circuits and systems, and energy conversion.
This work was partially supported by Higher Education Quality Enhancement Project (HEQEP), UGC, Bangladesh, under Sub-project “Research in Digital Image Processing,” Department of EEE, KUET, Khulna-9203, Bangladesh.
The authors declare that they have no competing interests.
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