Segmentation of CT Lung Images Using FCM with Active Contour and CNN Classifier

Objective: Lung cancer is one of the unsafe diseases for human which reduces the patient life time. Generally, most of the lung cancers are identified after it has been spread into the lung parts and moreover it is difficult to find the lung cancer at the early stage. It requires radiologist and special doctors to find the tumoral tissue of the lung cancer. For this reason, the recommended work helps to segment the tumoral tissue of CT lung image in an effective way. Methods:  The research work uses hybrid segmentation technique to separate the lung cancer cells to diagnose the lung tumour. It is a technique which combines active contour along with Fuzzy c means to diagnose the tumoral tissue. Further the segmented portion was trained by Convolutional Neural Network (CNN) in order to classify the segmented region as normal or abnormal. Results: The evaluation of the proposed method was done by analyzing the results of test image with the ground truth image. Finally, the results of the implemented technique provided good accuracy, Peak signal to noise ratio (PSNR), Mean Square Error (MSE) value. In future the other techniques can be utilized to improve the details before segmentation. The proposed work provides 96.67 % accuracy. Conclusion: Hybrid segmentation technique involves several steps like preprocessing, binarization, thresholding, segmentation and feature extraction using GLCM.     

基于模糊连接度的FCM分割方法在医学图像分析中的应用

图像分割的一个重要应用领域是医学图像的分割.我们针对医学图像的模糊特点和实际应用的要求,结合模糊连接度阈值分割和模糊C均值聚类两种分割方法的优点,提出一种新的交互式医学图像分割方法.首先计算整幅图像的模糊连接度,通过闽值分割提取出感兴趣的对象,并将模糊连接度作为图像的冗余特征;然后在由冗余特征和原图像特征构成的二维聚类空间中,利用模糊C-均值聚类方法优化上一步骤的分割结果,提高分割准确度.我们以CT和MR图像为实验对象进行了验证,实验结果表明这是一个有效的方法.     

Active Contour Based Segmentation and Classification for Pleura Diseases Based on Otsu’s Thresholding and Support Vector Machine (SVM)

Objective: Generally, lung cancer is the abnormal growth of cells that originates in one or both lungs. Finding thepulmonary nodule helps in the diagnosis of lung cancer in early stage and also increase the lifetime of the individual.Accurate segmentation of normal and abnormal portion in segmentation is challenging task in computer-aided diagnostics.Methods: The article proposes an innovative method to spot the cancer portion using Otsu’s segmentation algorithm. Itis followed by a Support Vector Machine (SVM) classifier to classify the abnormal portion of the lung image. Results:The suggested methods use the Otsu’s thresholding and active contour based segmentation techniques to locate theaffected lung nodule of CT images. The segmentation is followed by an SVM classifier in order to categorize theaffected portion is normal or abnormal. The proposed method is suitable to provide good and accurate segmentationand classification results for complex images. Conclusion: The comparative analysis between the two segmentationmethods along with SVM classifier was performed. A classification process based on active contour and SVM techniquesprovides better than Otsu’s segmentation for complex lung images.     

Image Registration based Cervical Cancer Detection and Segmentation Using ANFIS Classifier

Cervical cancer is the leading cancer in women around the world. In this paper, Adaptive Neuro Fuzzy InferenceSystem (ANFIS) classifier based cervical cancer detection and segmentation methodology is proposed. This proposedsystem consists of the following stages as Image Registration, Feature extraction, Classifications and Segmentation.Fast Fourier Transform (FFT) is used for image registration. Then, Grey Level Co-occurrence Matrix (GLCM), Greylevel and trinary features are extracted from the registered cervical image. Next, these extracted features are trainedand classified using ANFIS classifier. Morphological operations are now applied over the classified cervical imageto detect and segment the cancer region in cervical images. Simulations on large cervical image dataset demonstratethat the proposed cervical cancer detection and segmentation methodology outperforms the state of-the-art methods interms of sensitivity, specificity and accuracy.     

Validation of Segmented Brain Tumor from MRI Images Using 3D Printingthe

Background: Early diagnosis of a brain tumor is important for improving the treatment possibilities. Manually segmenting the tumor from the volumetric data is time-consuming, and the visualization of the tumor is rather challenging. Methods: This paper proposes a user-guided brain tumour segmentation from MRI (Magnetic Resonance Imaging) images developed using Medical Imaging Interaction Toolkit (MITK) and printing the segmented object using the 3D printer for tumour quantification. The proposed method includes segmenting the tumour interactively using connected threshold method, then printing the physical object from the segmented volume of interest. Then the distance between two voxels was measured using electronic callipers on the 3D volume in a specific direction. And next, the same distance was measured in the same direction on the 3D printed object. Results: The technique was tested with n=5 samples (20 readings) of brain MRI images from RIDER Neuro MRI dataset of National Cancer Institute. MITK provides various tools that enable image visualization, registration, and contouring. We were able to achieve the same measurements using both the approaches and this has been tested statistically with paired t-test method. Through this and the observer’s opinion, the accuracy of the segmentation was proved. Conclusion: When the difference in measurement of tumor volume through the electronic calipers and with 3D printed object equates to zero, proves that the segmentation technique is accurate. This helps to delineate the tumor more accurately during radio therapy.     

一种半监督的彩色图像分割方法

提出一种基于半监督EM聚类的彩色图像分割方法, 算法利用了有限的人工信息, 即在图像上点击有限的几个点以标识对应区域之间的关系, 从而得到满足给定限制的精确图像分割结果。算法首先对图像进行量化处理, 而后在量化后的色彩空间中集成先验的分割信息进行色彩聚类。实验表明算法运行速度快, 分割效果好, 具有很高的应用价值。     

An Automatic Bone Disorder Classification Using Hybrid Texture Feature Extraction with Bone Mineral Density

A novel approach has been proposed to classify bone disorders for classifying the radiographic bone image asnormal, Osteopenia and Osteoporosis. The proposed system consists of three major stages to predict the accurate bonedisorder classification. In the first stage, image preprocessing is performed where bilateral filtering is applied to removenoise and to enhance the image quality. Then, the image is fed to Otsu based segmentation approach for segmentingthe abnormal area of the bone image. In the second stage, Discrete Wavelet Transform (DWT) is used to the segmentedimage. Once the image gets segmented then, the Gray-Level Co-occurrence Matrix (GLCM) method is applied to extractthe features in terms of statistical texture-based. Further the image which is applied to Principle Component Analysis(PCA) to reduce size of the feature vector. Besides, Bone Mineral Density (BMD) feature namely calcium volume isestimated from abnormal region in the segmented bone image and it is concatenated with the extracted texture featuresto obtain the final feature vectors. In the final stage, the Multi-class Support Vector Machine (MSVM) takes featurevectors as a inputto classify bone disorders. The simulation result demonstrates that the proposed system achieved theaccuracy of 95.1% and sensitivity of 96.15%.     

Deep Convolution Neural Network for Malignancy Detection and Classification in Microscopic Uterine Cervix Cell Images

Objective: Automated Pap smear cervical screening is one of the most effective imaging based cancer detection tools used for categorizing cervical cell images as normal and abnormal. Traditional classification methods depend on hand-engineered features and show limitations in large, diverse datasets. Effective feature extraction requires an efficient image preprocessing and segmentation, which remains prominent challenge in the field of Pathology. In this paper, a deep learning concept is used for cell image classification in large datasets. Methods: This relatively proposed novel method, combines abstract and complicated representations of data acquired in a hierarchical architecture. Convolution Neural Network (CNN) learns meaningful kernels that simulate the extraction of visual features such as edges, size, shape and colors in image classification. A deep prediction model is built using such a CNN network to classify the various grades of cancer: normal, mild, moderate, severe and carcinoma. It is an effective computational model which uses multiple processing layers to learn complex features. A large dataset is prepared for this study by systematically augmenting the images in Herlev dataset. Result: Among the three sets considered for the study, the first set of single cell enhanced original images achieved an accuracy of 94.1% for 5 class, 96.2% for 4 class, 94.8% for 3 class and 95.7% for 2 class problems. The second set includes contour extracted images showed an accuracy of 92.14%, 92.9%, 94.7% and 89.9% for 5, 4, 3 and 2 class problems. The third set of binary images showed 85.07% for 5 class, 84% for 4 class, 92.07% for 3 class and highest accuracy of 99.97% for 2 class problems. Conclusion: The experimental results of the proposed model showed an effective classification of different grades of cancer in cervical cell images, exhibiting the extensive potential of deep learning in Pap smear cell image classification.     

Lung Lesion Detection in CT Scan Images Using the Fuzzy Local Information Cluster Means (FLICM) Automatic Segmentation Algorithm and Back Propagation Network Classification

Lung cancer is a frequently lethal disease often causing death of human beings at an early age because of uncontrolled cell growth in the lung tissues. The diagnostic methods available are less than effective for detection of cancer. Therefore an automatic lesion segmentation method with computed tomography (CT) scans has been developed. However it is very difficult to perform automatic identification and segmentation of lung tumours with good accuracy because of the existence of variation in lesions. This paper describes the application of a robust lesion detection and segmentation technique to segment every individual cell from pathological images to extract the essential features. The proposed technique based on the FLICM (Fuzzy Local Information Cluster Means) algorithm used for segmentation, with reduced false positives in detecting lung cancers. The back propagation network used to classify cancer cells is based on computer aided diagnosis (CAD).     

Atlas-based automatic segmentation of MR images: validation study on the brainstem in radiotherapy context

PURPOSE: Brain tumor radiotherapy requires the volume measurements and the localization of several individual brain structures. Any tool that can assist the physician to perform the delineation would then be of great help. Among segmentation methods, those that are atlas-based are appealing because they are able to segment several structures simultaneously, while preserving the anatomy topology. This study aims to evaluate such a method in a clinical context. METHODS AND MATERIALS: The brain atlas is made of two three-dimensional (3D) volumes: the first is an artificial 3D magnetic resonance imaging (MRI); the second consists of the segmented structures in this artificial MRI. The elastic registration of the artificial 3D MRI against a patient 3D MRI dataset yields an elastic transformation that can be applied to the labeled image. The elastic transformation is obtained by minimizing the sum of the square differences of the image intensities and derived from the optical flow principle. This automatic delineation (AD) enables the mapping of the segmented structures onto the patient MRI. Parameters of the AD have been optimized on a set of 20 patients. Results are obtained on a series of 6 patients' MRI. A comprehensive validation of the AD has been conducted on performance of atlas-based segmentation in a clinical context with volume, position, sensitivity, and specificity that are compared by a panel of seven experimented physicians for the brain tumor treatments. RESULTS: Expert interobserver volume variability ranged from 16.70 cm(3) to 41.26 cm(3). For patients, the ratio of minimal to maximal volume ranged from 48% to 70%. Median volume varied from 19.47 cm(3) to 27.66 cm(3) and volume of the brainstem calculated by AD varied from 17.75 cm(3) to 24.54 cm(3). Medians of experts ranged, respectively, for sensitivity and specificity, from 0.75 to 0.98 and from 0.85 to 0.99. Median of AD were, respectively, 0.77 and 0.97. Mean of experts ranged, respectively, from 0.78 to 0.97 and from 0.86 to 0.99. Mean of AD were, respectively, 0.76 and 0.97. CONCLUSIONS: Results demonstrate that the method is repeatable, provides a good trade-off between accuracy and robustness, and leads to reproducible segmentation and labeling. These results can be improved by enriching the atlas with the rough information of tumor or by using different laws of deformation for the different structures. Qualitative results also suggest that this method can be used for automatic segmentation of other organs such as neck, thorax, abdomen, pelvis, and limbs.     

Deep Learning Based Skin Lesion Segmentation and Classification of Melanoma Using Support Vector Machine (SVM)

Objective: The main objective of this study is to improve the classification performance of melanoma using deeplearning based automatic skin lesion segmentation. It can be assist medical experts on early diagnosis of melanomaon dermoscopy images. Methods: First A Convolutional Neural Network (CNN) based U-net algorithm is used forsegmentation process. Then extract color, texture and shape features from the segmented image using Local BinaryPattern ( LBP), Edge Histogram (EH), Histogram of Oriented Gradients (HOG) and Gabor method. Finally all thefeatures extracted from these methods were fed into the Support Vector Machine (SVM), Random Forest (RF), K-NearestNeighbor (KNN) and Naïve Bayes (NB) classifiers to diagnose the skin image which is either melanoma or benignlesions. Results: Experimental results show the effectiveness of the proposed method. The Dice co-efficiency valueof 77.5% is achieved for image segmentation and SVM classifier produced 85.19% of accuracy. Conclusion: In deeplearning environment, U-Net segmentation algorithm is found to be the best method for segmentation and it helps toimprove the classification performance.     

Brain Tumour Segmentation Using Convolutional Neural Network with Tensor Flow

Introduction: The determination of tumour extent is a major challenging task in brain tumour planning andquantitative evaluation. Magnetic Resonance Imaging (MRI) is one of the non-invasive technique has emanated asa front- line diagnostic tool for brain tumour without ionizing radiation. Objective: Among brain tumours, gliomasare the most common aggressive, leading to a very short life expectancy in their highest grade. In the clinical practicemanual segmentation is a time consuming task and their performance is highly depended on the operator’s experience.Methods: This paper proposes fully automatic segmentation of brain tumour using convolutional neural network.Further, it uses high grade gilomas brain image from BRATS 2015 database. The suggested work accomplishesbrain tumour segmentation using tensor flow, in which the anaconda frameworks are used to implement high levelmathematical functions. The survival rates of patients are improved by early diagnosis of brain tumour. Results: Hence,the research work segments brain tumour into four classes like edema, non-enhancing tumour, enhancing tumour andnecrotic tumour. Brain tumour segmentation needs to separate healthy tissues from tumour regions such as advancingtumour, necrotic core and surrounding edema. This is an essential step in diagnosis and treatment planning, both ofwhich need to take place quickly in case of a malignancy in order to maximize the likelihood of successful treatment.     

Automatic Approach for Cervical Cancer Detection and Segmentation Using Neural Network Classifier

Cervical cancer leads to major death disease in women around the world every year. This cancer can be cured if itis initially screened and giving timely treatment to the patients. This paper proposes a novel methodology for screeningthe cervical cancer using cervigram images. Oriented Local Histogram Technique (OLHT) is applied on the cervicalimage to enhance the edges and then Dual Tree Complex Wavelet Transform (DT-CWT) is applied on it to obtainmulti resolution image. Then, features as wavelet, Grey Level Co-occurrence Matrix (GLCM), moment invariantand Local Binary Pattern (LBP) features are extracted from this transformed multi resolution cervical image. Theseextracted features are trained and also tested by feed forward back propagation neural network to classify the givencervical image into normal and abnormal. The morphological operations are applied on the abnormal cervical imageto detect and segment the cancer region. The performance of the proposed cervical cancer detection system is analyzedin the terms of sensitivity, specificity, accuracy, positive predictive value, negative predictive value, Likelihood Ratiopositive, Likelihood ratio negative, precision, false positive rate and false negative rate. The performance measures forthe cervical cancer detection system achieves 97.42% of sensitivity, 99.36% of specificity, 98.29% of accuracy, PPVof 97.28%, NPV of 92.17%, LRP of 141.71, LRN of 0.0936, 97.38 % precision, 96.72% FPR and 91.36% NPR. Fromthe simulation results, the proposed methodology outperforms the conventional methodologies for cervical cancerdetection and segmentation process.     

A mathematical theory of shape and neuro-fuzzy methodology-based diagnostic analysis: a comparative study on early detection and treatment planning of brain cancer

Background

Investigation of brain cancer can detect the abnormal growth of tissue in the brain using computed tomography (CT) scans and magnetic resonance (MR) images of patients. The proposed method classifies brain cancer on shape-based feature extraction as either benign or malignant. The authors used input variables such as shape distance (SD) and shape similarity measure (SSM) in fuzzy tools, and used fuzzy rules to evaluate the risk status as an output variable. We presented a classifier neural network system (NNS), namely Levenberg–Marquardt (LM), which is a feed-forward back-propagation learning algorithm used to train the NN for the status of brain cancer, if any, and which achieved satisfactory performance with 100% accuracy.

Methods

The proposed methodology is divided into three phases. First, we find the region of interest (ROI) in the brain to detect the tumors using CT and MR images. Second, we extract the shape-based features, like SD and SSM, and grade the brain tumors as benign or malignant with the concept of SD function and SSM as shape-based parameters. Third, we classify the brain cancers using neuro-fuzzy tools. In this experiment, we used a 16-sample database with SSM (μ) values and classified the benignancy or malignancy of the brain tumor lesions using the neuro-fuzzy system (NFS).

Results

We have developed a fuzzy expert system (FES) and NFS for early detection of brain cancer from CT and MR images. In this experiment, shape-based features, such as SD and SSM, were extracted from the ROI of brain tumor lesions. These shape-based features were considered as input variables and, using fuzzy rules, we were able to evaluate brain cancer risk values for each case. We used an NNS with LM, a feed-forward back-propagation learning algorithm, as a classifier for the diagnosis of brain cancer and achieved satisfactory performance with 100% accuracy. The proposed network was trained with MR image datasets of 16 cases. The 16 cases were fed to the ANN with 2 input neurons, one hidden layer of 10 neurons and 2 output neurons. Of the 16-sample database, 10 datasets for training, 3 datasets for validation, and 3 datasets for testing were used in the ANN classification system. From the SSM (µ) confusion matrix, the number of output datasets of true positive, false positive, true negative and false negative was 6, 0, 10, and 0, respectively. The sensitivity, specificity and accuracy were each equal to 100%.

Conclusion

The method of diagnosing brain cancer presented in this study is a successful model to assist doctors in the screening and treatment of brain cancer patients. The presented FES successfully identified the presence of brain cancer in CT and MR images using the extracted shape-based features and the use of NFS for the identification of brain cancer in the early stages. From the analysis and diagnosis of the disease, the doctors can decide the stage of cancer and take the necessary steps for more accurate treatment. Here, we have presented an investigation and comparison study of the shape-based feature extraction method with the use of NFS for classifying brain tumors as showing normal or abnormal patterns. The results have proved that the shape-based features with the use of NFS can achieve a satisfactory performance with 100% accuracy. We intend to extend this methodology for the early detection of cancer in other regions such as the prostate region and human cervix.

     

Aiding the Digital Mammogram for Detecting the Breast Cancer Using Shearlet Transform and Neural Network

Objective: Breast Cancer is the most invasive disease and fatal disease next to lung cancer in human. Early detectionof breast cancer is accomplished by X-ray mammography. Mammography is the most effective and efficient techniqueused for detection of breast cancer in women and also to improve the breast cancer prognosis. The numbers of imagesneed to be examined by the radiologists, the resulting may be misdiagnosis due to human errors by visual Fatigue.In order to avoid human errors, Computer Aided Diagnosis is implemented. In Computer Aided Diagnosis system,number of processing and analysis of an image is done by the suitable algorithm. Methods: This paper proposed atechnique to aid radiologist to diagnosis breast cancer using Shearlet transform image enhancement method. Similar towavelet filter, Shearlet coefficients are more directional sensitive than wavelet filters which helps detecting the cancercells particularly for small contours. After enhancement of an image, segmentation algorithm is applied to identify thesuspicious region. Result: Many features are extracted and utilized to classify the mammographic images into harmfulor harmless tissues using neural network classifier. Conclusions: Multi-scale Shearlet transform because more details ondata phase, directionality and shift invariance than wavelet based transforms. The proposed Shearlet transform gives multi resolution result and generate malign and benign classification more accurate up to 93.45% utilizing DDSM database.     

MRI分割在肿瘤放疗中的应用研究

MRI技术无辐射,软组织分辨率高,因此MR引导下的放疗现已成为放疗领域的热点研究工作。放疗中精确勾画靶区是非常关键的,目前多为手动分割,耗时、主观且缺乏一致性,自动图像分割可以在不降低分割质量前提下提高效率和可重复性。本文综述了在放疗中MRI的自动分割方法,对不同放疗部位包括前列腺、鼻咽癌、脑部肿瘤以及其他器官,就自动分割目标、挑战和方法进行分析和讨论。     

一种基于聚类的消失点自动测量方法

在人工环境中有很多平行的直线和相互正交的平面。本文提出了一种对相机拍摄的人工环境中的中远距离场景图像,自动获取其空间中三个方向相互正交的消失点的方法。利用中远距离场景中的平行直线在图像中倾角接近这一特点对图像中的边缘线段进行聚类初始化,并利用空间中平行直线在图像中相交于消失点这一事实进行聚类。三个消失点的三角形标准以及相机焦距标准将有助于消除虚假消失点。实验结果表明,该方法对中远距离场景的消失点检测可行、快速。     

Automatic intraocular lens segmentation and detection in optical coherence tomography images

We present a new algorithm for automatic segmentation and detection of an accommodative intraocular lens implanted in a biomechanical eye model. We extracted lens curvature and position. The algorithm contains denoising and fan correction by a multi-level calibration routine. The segmentation is realized by an adapted canny edge detection algorithm followed by a detection of lens surface with an automatic region of interest search to suppress non-optical surfaces like the lens haptic. The optical distortion of lens back surface is corrected by inverse raytracing. Lens geometry was extracted by a spherical fit. We implemented and demonstrated a powerful algorithm for automatic segmentation, detection and surface analysis of intraocular lenses in vitro. The achieved accuracy is within the expected range determined by previous studies. Future improvements will include the transfer to clinical anterior segment OCT devices.     

基于形态学多尺度修正的脑水肿区域分割

脑CT图像组织结构较为复杂且灰度不均匀,直接采用分水岭分割会导致较严重的过分割,采用阈值标记控制的分水岭分割通过限定可分割区域,可以较好地减轻过分割,但容易出现目标标记不准确的问题。为此提出了一种基于形态学多尺度修正的标记控制分水岭分割方法,首先对原始图像进行线性拉伸和指数增强,提高水肿与正常区域的对比度;然后在形态学梯度图像基础上,根据不同像素梯度值确定结构元素的大小,对图像进行形态学多尺度修正,以消除局部极小区域,保证修正过程中目标轮廓不发生较大偏移;最后采用标记控制的分水岭变换对图像进行分割。实验结果表明,该方法可对脑部水肿区域进行较精确的分割。