- Research Article
- Open Access
Brain medical image diagnosis based on corners with importance-values
© The Author(s). 2017
- Received: 6 June 2017
- Accepted: 1 November 2017
- Published: 21 November 2017
Brain disorders are one of the top causes of human death. Generally, neurologists analyze brain medical images for diagnosis. In the image analysis field, corners are one of the most important features, which makes corner detection and matching studies essential. However, existing corner detection studies do not consider the domain information of brain. This leads to many useless corners and the loss of significant information. Regarding corner matching, the uncertainty and structure of brain are not employed in existing methods. Moreover, most corner matching studies are used for 3D image registration. They are inapplicable for 2D brain image diagnosis because of the different mechanisms. To address these problems, we propose a novel corner-based brain medical image classification method. Specifically, we automatically extract multilayer texture images (MTIs) which embody diagnostic information from neurologists. Moreover, we present a corner matching method utilizing the uncertainty and structure of brain medical images and a bipartite graph model. Finally, we propose a similarity calculation method for diagnosis.
Brain CT and MRI image sets are utilized to evaluate the proposed method. First, classifiers are trained in N-fold cross-validation analysis to produce the best θ and K. Then independent brain image sets are tested to evaluate the classifiers. Moreover, the classifiers are also compared with advanced brain image classification studies. For the brain CT image set, the proposed classifier outperforms the comparison methods by at least 8% on accuracy and 2.4% on F1-score. Regarding the brain MRI image set, the proposed classifier is superior to the comparison methods by more than 7.3% on accuracy and 4.9% on F1-score. Results also demonstrate that the proposed method is robust to different intensity ranges of brain medical image.
In this study, we develop a robust corner-based brain medical image classifier. Specifically, we propose a corner detection method utilizing the diagnostic information from neurologists and a corner matching method based on the uncertainty and structure of brain medical images. Additionally, we present a similarity calculation method for brain image classification. Experimental results on two brain image sets show the proposed corner-based brain medical image classifier outperforms the state-of-the-art studies.
- Brain medical image diagnosis
- Corner detection
- Multilayer texture images
- Corner matching
- Bipartite graph
Image-based brain disorder diagnosis has attracted increasing interest in computer-assisted interventions . This study has been handled efficiently with the application of machine learning methods . Among numerous machine learning methods, deep learning has been showing the state-of-the-art performance in the recent years. It has been applied in many fields of computer vision, natural language process, and medical image analysis. However, deep learning is still limited because of its vast number of network parameters that must be learned from a large amount of data. It is noteworthy that data sets collected in brain imaging studies are commonly very small. Thus deep learning is still a challenging method in brain image analysis. In the prevalent framework of machine learning for brain imaging data diagnosis, feature extraction is an essential step . Rong et al. have proposed a symmetry-based brain CT image classification method consisting of three stages . First, the weak symmetry of the histograms of two hemispheres is used to classify brain CT images into normal or abnormal categories. Second, the strong symmetry of the textures between two hemispheres in abnormal images are extracted to locate lesions. At last, the abnormal images are classified into benign or malignant ones based on the features of lesions. A few caveats exist in this method. For instance, for abnormal images with similar lesions in both hemispheres or with lesions in the middle of brain, the weak or strong symmetry features are insufficient to distinguish normal and abnormal images. Ding et al. have proposed a joint feature selection method from voxel-based morphometry (VBM) and texture analysis to distinguish Alzheimer’s disease (AD) from the normal controls . However, this method is not stable for brain disease diagnosis using brain medical images with different intensity ranges.
Corners are the small points of interest with variation in two dimensions . They are essential features of images and play a critical role in grasping objects. Tremendous progress has been made over corner research [5–8], but many fundamental problems are still open, such as corner detection, matching, and corner-based image classification.
As a fundamental and important step in many vision tasks such as image matching, recognition and tracking [5, 6, 9], corner detection has been well studied for many years. Existing methods are divided into three main categories: gray intensity-based, contour-based, and model-based methods . One of the most classical gray intensity-based methods is the Harris method . It is broadly applied in many cases due to its advantages of rotation, translation, and illumination invariance . Nevertheless, Harris is prone to produce numerous useless corners when used over a whole grayscale image. In the field of medical image analysis, doctors generally pay considerable attention to the morphology of ROIs (Regions of Interest). Thus, corners detected from those uninteresting regions are prone to be useless. Moreover, Harris only stores the coordinates of corners, leading to the loss of important medical domain knowledge. Concretely, these corners do not preserve the uncertainty and structure of pixels in brain medical images . To address the above inadequacy, contour-based corner detection methods [13–15] might be considered. This type of methods first extracts curves of images using edge detectors, and then searches for the curvature maxima along those curves as corners. Though these methods detect corners on image contours to reduce useless corners, the extracted contours are regarded equally. This does not integrate doctors’ diagnostic information. Thus, the detected corners lose the essential domain knowledge. In terms of the model-based methods, existing methods are required to fit images into a predefined model. These methods are often limited to specific types of points . Moreover, these methods are time-consuming and detected corners do not have the significant diagnostic information either.
Corner matching is to realize the correspondences of two corner sets by estimating the transformation from a corner set to the other . Existing corner matching studies mainly fall into two categories: rigid and non-rigid. Rigid methods are mainly developed based on the classical iterative closest point (ICP) , which uses affine transformations to find the closest corresponding points between two surfaces. Nonetheless, methods based on the ICP do not fully capture the anatomical variability, especially comparing shape with significant differences . Thus, rigid methods are less applicable in real-world problems . Non-rigid corner matching methods generally produce an initial matched corner pair sequence first. Then these methods evaluate the transformation between initial matched corner pair sequences to generate a final matched corner pair sequence. Yi et al.  have used l 1-norm to formalize the transformation between initial matched corner pair sequences. However, they have not argued the generation of initial matched corner pair sequences. Thus, the uncertainty of corners is not taken into account in the corner matching process. Myronen and Song  have proposed a probabilistic method to realize the alignment between corner sets. They regard corners as a group and move a group of corners coherently to keep the topological structure among corners. Since the deformations of human brains usually happen in local regions, coherent movement causes improper matching among corners from other regions. This would lead to the failure of the generation of the best correspondence. Thus, this method is inappropriate for corner matching in brain medical images. Moreover, the above corner matching methods are used for 3D image registration. They are to match corners of the images from the same object or person, unlike the corner matching of the brain medical images from different patients.
To address these problems, an automatic multilayer texture image (MTI) extraction method is first proposed for corner detection. The method integrates the diagnostic knowledge from neurologists and assigns each corner an importance-value representing the uncertainty of a pixel. Second, a corner matching method is presented combining the uncertainty and structure of brain medical images. It generates an initial matched corner pair sequence based on the definition of matched corner pairs and utilizes a bipartite graph model to reduce the redundancy of the initial matched corner pair sequence. At last, a similarity function is proposed based on the matched and unmatched corner pairs to make diagnoses for brain medical images. Experimental results on brain CT and MRI image sets show that the proposed corner-based brain medical image diagnosis method outperforms three state-of-the-art methods on accuracy and F1-score. Additionally, it is more robust to different intensity ranges of brain medical image.
Frequently used symbols
An original grayscale image
Normalized grayscale image
The number of the rows in GI
The number of the columns in GI
Multilayer texture image
A corner response threshold
A corner that is located in the mth row and nth column
A corner sequence
Mobility of the corner (m, n)
A matched corner pair
An initial matched corner pair sequence
The final matched corner pair sequence
The K of the KNN model
Brain medical image sets
Brain medical image normalization
Original brain medical images are usually produced in different sizes and angles and also display some useless information. These original brain images consist of three portions: background, skull and the intracranial portion. Background is useless for doctors’ diagnosis and always contains noise. Moreover, when doctors make diagnoses, they usually focus on the intracranial portion. Thus, these original images need to be preprocessed as follows: extracting the intracranial portion using the Canny operator  because of the clear disparity of the grayscale values between the skull and the intracranial portion, rotating the intracranial portion into the vertical direction using the method in , cropping the image based on the vertical external matrix of its intracranial portion and unifying image size into Row ×Column, where Row/Column is the mean of all the row/column numbers of the brain medical image set. Counting the image size of the two brain medical image sets seperately, we normalize all brain CT images into 285×260 and all the brain MRI images into 175×141.
Corner detection based on multilayer texture images
In the diagnostic process of brain medical images, doctors generally pay more attention to the morphology of the hypodense (dark) and hyperdense (bright) regions than that of other regions . That means different regions in brain medical image embody diverse diagnostic information. Thus, corners detected from these more hypodense and hyperdense regions are more essential than those detected from other regions. Under this guidance, a multilayer texture image (MTI) is first proposed to distinguish the significance of regions in brain medical images. Then, corners are detected on the MTI and are assigned different importance-values to show their significance. Thus, corners detected from MTIs employ certain diagnostic information.
Given a Row×Column GI, its MTI is comprised of multiple layers of textures. Each layer of textures have the same pixel value. The pixel values denote the significance of pixels and the pixel value and the significance of pixels have a direct correlation. The MTI extraction method is similar to the Canny method . It contains smoothing an image, discovering its intensity gradient, determining the hysteresis double thresholds and generating edges based on the pair of double thresholds. However, the Canny method can only generate a binary texture image by using one pair of double thresholds, and the binary texture image cannot describe the significance of different regions. To handle this, we propose an automatic MTI generation method by using multiple pairs of double thresholds based on the intensity gradient of a GI.
[highTh i , lowTh i ] is the ith pair of double thresholds and is used to extract textures in the ith layer.
In Eqs. 1 and 2, s i represents the index value of H(j) and its maximum value is Bn. The histogram value of each bin is the number of pixels with several continuous intensity gradient values. Provided the maximum intensity gradient value of an image is M, each bin counts the number of pixels with M/Bn continuous intensity gradient values. R i is a decimal iterating from R 1 to 0 with 0.1 drop. It controls the ratio of pixel number whose index gradient values are ordered in descending way. For each R i , Eq. 1 returns a s i value. This indicates that the corresponding pixels in the first s i bins of H(j) have clearer edges than that in the rear bins. Thus, s i dividing the maximum bin number Bn is used to generate the thresholds, as shown in Eq. 2.
In our study, the Bn and rTh are set to 64 and 0.3, respectively. Generally, the morphology of the ventricles is the clearest part in a brain medical image. Based on experiments, R 1 is set to 0.8 to produce the first pair of double thresholds [highTh 1, lowTh 1], which is used to extract the first layer of textures containing the edges of ventricles. Then R i is decreased by 0.1 each time until 0 to produce the remaining pairs of double thresholds. Next these pairs of double thresholds are used to extract the remaining (N-1) layers of textures.
It is notable that the pairs of double thresholds are generated in a descending order, and the preceding thresholds are used to extract the textures of clearer regions. Based on this, a MTI is created by setting MTI(m, n)=1/i when textures in the ith layer pass the pixel (m, n) yet textures in the preceding (i-1) layers do not pass (m, n). When no texture passes a pixel, that means the pixel has no specific diagnostic information and is thus set to 0 as the background. From the MTI extraction process, the textures in preceding layers have larger importance-values than those in rear layers. This indicates that the textures in preceding layers embody more essential diagnostic information. It can be seen that MTIs can not only describe the edges of ROIs but also can distinguish the significance of these ROIs.
After a MTI is generated, corners are detected on the MTI utilizing the Harris algorithm , where the corner response threshold θ is discussed in the experiment. Besides storing the coordinate of the corner (m, n), we also preserve MTI(m, n) as the importance-value of (m, n).
Corner matching based on the uncertainty and structure of corners
Based on the mobility of corners, matched corner pairs are generated.
where dis(c, c′)=||((m, n), (m′, n′)||2.
Initial matched corner pair sequences are generated based on Definition 1. Nevertheless, the sequences have redundant matched corner pairs. In order to remove the redundancy and meanwhile preserve information to maximum extent, we propose a finial matched corner pair sequence.
The first case in Definition 2 is to eliminate redundant matched corner pairs. The second and third cases ensure to preserve the longest and most matched corner pairs.
In order to realize CM, a biparitite graph G=(V, E, We) is generated based on CI. The problem of generating the final matched corner pair sequence is transferred to solve the maximum matching of G, which has been solved by using the Hungarian method [24, 25].
Algorithm 2 outlines the main ideas of the proposed corner matching method. The vertex number of G is (|C|+|C′|), the maximum edge number of G is |C|×|C′|, and the time complexity of the Hungarian method is Ο(|C|×|C′|×(|C|+|C′|)), thus the time complexity of our corner matching is Ο(|C|×|C′|× (|C|+|C′|)).
Corner-based brain image classification
In Eq. 5, MD(c Mi , c Mj ′) is the matched degree of [c Mi , c Mj ′]. It indicates that the smaller the distance of [c Mi , c Mj ′] is, the larger the matched degree of [c Mi , c Mj ′] will be. W(I, I′) is a weight, where |C| and |C′| are the number of corners detected from I and I′, respectively; |(|C|-|CM|)-(|C′|-|CM|)| is the number diversity of the unmatched corners between I and I′. The value of W(I, I′) indicates that a small number of the unmatched corners between I and I′ makes a large effect on the similarity between images.
Based on Eq. 5, I can be classified using the KNN model. Specifically, the K most similar images of I among the training images are selected first. Then I is assigned a label whose number among the K most-similar images is the largest.
We evaluated the proposed corner-based brain medical image classification method (denoted as the Corner-based method) on the two brain medical image sets: Dct and Dmri. All the experiments were implemented on a computer with an Intel Core (7) processor of 2.60 GHZ and a RAM of 16GB. The development environment includes Eclipse 4.6 and Matlab 2015a.
Evaluation of the corner-based brain medical image classification
All brain medical image sets
training set (training and validation images)
To validate the effectiveness of the proposed method, the method is compared with three baseline brain medical image classification methods: Harris-based method, Texture-based method and Symmetry-based method.
The other two comparison methods are based on the methods in [3, 4]. In this study, all the images are 2-D. Since the VBM in  is 3-D features, only the texture features are used in the comparison experiment. This method is referred as the Texture-based method. There is no threshold in the Texture-based method according to . In , brain medical images are classified into “Normal” or “Abnormal” categories in the first stage by using the symmetry of brain medical images. The method applied in the first stage of  is used as another comparison method. It is denoted as the Symmetry-based method. Based on , the threshold of the Symmetry-based method is set to 0.88 for brain CT image classification.
Performance of the four comparison methods on Dct
Performance of the four comparison methods on Dmri
Runtime(s) of the four comparison methods on Dct and Dmri
In this study, we develop a robust corner-based brain medical image classifier. First, we propose a corner detection method combing diagnostic information from neurologists. This method consists of MTI extraction and corners detection on MTIs. Second, we present the definition of matched corner pairs and put forward a bipartite model to generate the final matched corner pair sequence. Finally, we propose a similarity calculation method based on the matched and unmatched corner pairs for brain medical image diagnosis. We demonstrate that the proposed corner-based classifier can efficiently achieve the highest accuracy and F1-score for brain medical image classification and is robust to the intensity ranges of brain medical images caused by different brain imaging modalities.
Based on the priori diagnostic information that different regions in brain medical images have diverse diagnostic information, we extract MTIs. Specifically, we utilize multiple groups of thresholds to separately extract the multilayers of textures from different regions and assign these textures different significance to form MTIs. Then, corners are detected on MTIs and are assigned specific importance-values based on the corresponding significance in MTIs. Thus, these detected corners can not only describe the morphology of brain medical images but also demonstrate their specific significance. Moreover, since brain medical images have the uncertainty and structure, corners also inherit these characteristics. The uncertainty makes corners have mobility, yet the structure limits the movable ranges of corners. Specifically, the movable ranges of significant corners are relative small yet the movable ranges of insignificant corners are comparative large. Based on the movable ranges and importance-values of corners, we define a matched corner pair to produce an initial matched corner pair sequence. We further propose a bipartite graph to reduce the redundancy of the initial matched corner pair sequence and generate the final matched corner pair sequence for classification. The proposed method takes advantage of the priori diagnostic information and the characteristics of brain medical images. This makes it achieve great performance and robust to different intensity ranges of brain medical images.
Different from the Texture-based method  and Symmetry-based method , both the proposed method and the Harris-based method utilizes corners as features to classify brain medical images. Moreover, compared with the Harris-based method, the proposed method assigns corners importance-values by employing the priori diagnostic information. In Dct, the proposed method outperforms the three comparison methods in terms of accuracy and F1-score. The Harris-based method achieves better accuracy and F1-score than the Texture-based method and attains competitive accuracy and F1-score compared with the Symmetry-based method. In Dmri, both the proposed method and the Harris-based method gain higher accuracy and F1-score compared with the Texture-based method and the Symmetry-based method. The proposed method still outweighs the Harris-based method. These results indicate that (1) corners are discriminative features for both brain CT and brain MRI image classification; (2) employing the diagnostic information and the characteristics of brain medical images enhances the performance of the proposed method. These results are because corners are the key points of the morphology of images and the morphology of organs or lesions in brain medical images are the main evidence of doctors’ diagnosis. Though textures can reflect the morphology of images, the Texture-based method regards the textures of different regions equally while the proposed method distinguish the significance of the textures of different regions. Moreover, in terms of the proposed method, its accuracy and F1-score on Dmri decrease 4.4% and 8.3% compared with that on Dct. In terms of the Harris-based method, its accuracy and F1-score on Dmri decrease 4.9% and 10.1% compared with that on Dct. In terms of the Symmetry-based method, its accuracy and F1-score on Dmri decrease 11.1% and 21.4% compared with that on Dct. In terms of the Texture-based method, its F1-score on Dmri decreases 66.7%; though its accuracy keep the same, the accuracy values on both Dct and Dmri are 50%, which is a random value in binary classification. These results indicate that (1) the corner-based methods (i.e., the proposed method and the Harris-based method) are more robust to different intensity ranges of brain medical images caused by the different imaging modalities in this study (the maximum intensity value of Dct is 256 and the Dmri’s is 65536); (2) employing the diagnostic information in the proposed method enhances the robustness compared with the Harris-based method. These are because of the following reasons: during the process of MTI extraction based on the diagnostic information, different layers of textures are assigned different importance-values to reflect the diagnostic information. These importance-values are calculated based on the histogram of an image’ gradient values not an image’s intensity values. Since corners are detected on MTIs, this alleviates the dependence of the proposed corner detection method on the intensity values of images. Thus, the proposed method can endure the variability of the intensity ranges caused by different brain imaging modalities in this study. Furthermore, the proposed method is more efficient than the Harris-based method and Texture-based method yet less efficient than the Symmetry-based method. However, the proposed method does not use any optimization, which will be handled in our future work.
In this study, we first propose a corner detection method combining the diagnostic information of brain medical images. It consists of two steps: MTI extraction and corner detection based on MTIs. These detected corners are assigned different importance-values to distinguish their significance. Second, we put forward a corner matching method. It produces an initial matched corner pair sequence based on the uncertainty and structure of brain medical images and further generates a final matched corner pair sequence via a bipartite graph model. Finally, a similarity function is proposed based on the final matched corner pair sequence and is used to make diagnoses for brain medical images. Experimental results on brain CT and MRI medical image sets show that the proposed corner-based brain medical image diagnosis method outperforms the three comparison methods, and is more robust to the intensity ranges of brain medical images caused by different brain imaging modalities.
Recent studies have shown multimodal medical images can provide complementary information to enhance diagnostic accuracy. In the future, we will focus on corner detection and matching based on the multimodal brain medical images. Moreover, the proposed method does not take parallel computing into account, we will boost the efficiency of the proposed method in a parallel way.
The authors would like to thank the ADNI database (http://adni.loni.usc.edu/) for the collection of brain MRI images.
the Alzheimer’s Disease Neuroimaging Initiativea
aData used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc. edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at:http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
The paper is partly supported by the National Natural Science Foundation of China under Grant No. 61672181, No. 51679058. Natural Science Foundation of Heilongjiang Province under Grant No. F2016005. Council Scholarship of China. Data collection and sharing for this study was funded by the ADNI (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Bioengineering.
Availability of data and materials
Dmri dataset supporting the conclusions of this article is available in the ADNI repository [http://adni.loni.usc.edu/].
Dct dataset supporting the conclusions of this article is available from the corresponding author upon reasonable request.
LG, HP QL, XX and ZZ conceived of the study. LG, HP and JH performed the collection and label annotation of brain CT images. LG and XZ carried out the experiment. GL, HP and JH analyzed the results. All authors read and approved the final manuscript.
Ethics approval and consent to participate
For Dmri, ethics approval is not required as the human data were publicly available by ADNI website, and all the data are not identifiable. For Dct, informed consent was obtained in accordance with institutional policies in use in each country.
Consent for publication
The authors declare that they have no competing interests.
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