A review on brain tumor segmentation of MRI images.

Brain tumor segmentation based on a new threshold approach

In this work, a new framework for breast cancer image segmentation and classification is proposed. Different models including InceptionV3, DenseNet121, ResNet50, VGG16 and MobileNetV2 models, are applied to classify Mammographic Image Analysis Society (MIAS), Digital Database for Screening Mammography (DDSM) and the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) into benign and malignant. Moreover, the trained modified U-Net model is utilized to segment breast area from the mammogram images. This method will aid as a radiologist's assistant in early detection and improve the efficiency of our system. The Cranio Caudal (CC) vision and Mediolateral Oblique (MLO) view are widely used for the identification and diagnosis of breast cancer. The accuracy of breast cancer diagnosis will be improved as the number of views is increased. Our proposed frame work is based on MLO view and CC view to enhance the system performance. In addition, the lack of tagged data is a big challenge. Transfer learning and data augmentation are applied to overcome this problem. Three mammographic datasets; MIAS, DDSM and CBIS-DDSM, are utilized in our evaluation. End-to-end fully convolutional neural networks (CNNs) are introduced in this paper. The proposed technique of applying data augmentation with modified U-Net model and InceptionV3 achieves the best result, specifically with the DDSM dataset. This achieves 98.87% accuracy, 98.88% area under the curve (AUC), 98.98% sensitivity, 98.79% precision, 97.99% F1 score, and a computational time of 1.2134 s on DDSM datasets.

Deep learning in mammography images segmentation and classification: Automated CNN approach

One of the most crucial tasks in any brain tumor detection system is the isolation of abnormal tissues from normal brain tissues. Interestingly, domain of brain tumor analysis has effectively utilized the concepts of medical image processing, particularly on MR images, to automate the core steps, i.e. extraction, segmentation, classification for proximate detection of tumor. Research is more inclined towards MR for its non-invasive imaging properties. Computer aided diagnosis or detection systems are becoming challenging and are still an open problem due to variability in shapes, areas, and sizes of tumor. The past works of many researchers under medical image processing and soft computing have made noteworthy review analysis on automatic brain tumor detection techniques focusing segmentation as well as classification and their combinations. In the manuscript, various brain tumor detection techniques for MR images are reviewed along with the strengths and difficulties encountered in each to detect various brain tumor types. The current segmentation, classification and detection techniques are also conferred emphasizing on the pros and cons of the medical imaging approaches in each modality. The survey presented here aims to help the researchers to derive the essential characteristics of brain tumor types and identifies various segmentation/classification techniques which are successful for detection of a range of brain diseases. The manuscript covers most relevant strategies, methods, their working rules, preferences, constraints, and their future snags on MR image brain tumor detection. An attempt to summarize the current state-of-art with respect to different tumor types would help researchers in exploring future directions.

A survey on brain tumor detection techniques for MR images

Brain cancer is caused by the population of abnormal cells called glial cells that takes place in the brain. Over the years, the number of patients who have brain cancer is increasing with respect to the aging population, is a worldwide health problem. The objective of this paper is to develop a method to detect the brain tissues which are affected by cancer especially for grade-4 tumor, Glioblastoma multiforme (GBM). GBM is one of the most malignant cancerous brain tumors as they are fast growing and more likely to spread to other parts of the brain. In this paper, Naive Bayes classification is utilized for recognition of a tumor region accurately that contains all spreading cancerous tissues. Brain MRI database, preprocessing, morphological operations, pixel subtraction, maximum entropy threshold, statistical features extraction, and Naive Bayes classifier based prediction algorithm are used in this research. The goal of this method is to detect the tumor area from different brain MRI images and to predict that detected area whether it is a tumor or not. When compared to other methods, this method can properly detect the tumor located in different regions of the brain including the middle region (aligned with eye level) which is the significant advantage of this method. When tested on 50 MRI images, this method develops 81.25% detection rate on tumor images and 100% detection rate on non-tumor images with the overall accuracy 94%.

Brain tumor detection based on Naïve Bayes Classification

The functionality of brain can be disrupted by brain tumor, which is an abnormal growth of tissue in brain or central spine. Due to undefined size, shape and location, detection of brain tumor from MRI (Magnetic Resonance Imaging) is a challenging and difficult task. Previous tumor segmentation methods were generally based on intensity enhancement techniques on T1-weighted image, which was appeared with gadolinium contrast agent on strictly uniform intensity patterns. This paper presents a new method based on Thresholding along with morphological image analysis techniques to detect brain tumor from MRI image. The image was first converted to grayscale and then noises were removed by applying different filtering techniques. The grayscale image was then converted to binary image adding 0.3 with the Otsu's threshold value to perfectly segment the tumor region. Afterwards, morphological operations were performed to detect the tumor that contains the brightest part of the MRI. The method suggested for detection was tested over 72 FLAIR images of 72 patients taken from BRATS Brain Tumor database, out of which the proposed algorithm was able to detect tumor from 61 images successfully. Experimental result showed an accuracy rate of 84.72% in detecting 61 patients Brain Tumor which is very much promising compares to other existing method. General Terms Image Segmentation, Medical Image Analysis, Digital Image Processing.

https://www.ijcaonline.org/archives/volume153/number10/sujan-2016-ijca-912177.pdf

A Segmentation based Automated System for Brain Tumor Detection

This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy ( 95.00 ± 0.57 %) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A z value equal to 0.97 ± 0.01 .

https://www.mdpi.com/2313-433X/5/9/76/pdf

Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier.

Early detection of microcalcification (MC) clusters plays a crucial role in enhancing breast cancer diagnosis. Two automated MC cluster segmentation techniques are proposed based on morphological operations that incorporate image decomposition and interpolation methods. For both approaches, initially the contrast between the background tissue and MC cluster was increased and subsequently morphological operations were used. Evaluation was based on the Dice similarity scores and the results of MC cluster classification. A total number of 248 (131 benign and 117 malignant) and 24 (12 benign and 12 malignant) biopsy-proven digitized mammograms were considered from the DDSM and MIAS databases, which showed a classification accuracy of \(94.48\pm 1.11\)% and \(100.00\pm 0.00\)% respectively.

Automatic Segmentation of Microcalcification Clusters

Automatic License Plate Recognition (ALPR) is an image processing technology which used to identify vehicles by their license plate. In this paper, an ALPR system is developed where adaptive resize and crop are used for better efficiency. For highlighting the license plate region image morphology, horizontal and vertical extraction has been used. Line Segmentation and Orientation (LSO) algorithm is proposed for segmentation which is also effective in efficient searching for signs of the license plate and finally template matching has been used for recognition. The proposed method works successfully with the recognition rate of 84.87%. Moreover, some license plates with different fonts of writings have been given true positive results which are not issued by the government. So, the proposed method will be helpful not only in license plate recognition but also in other field of Bangla optical character recognition.

https://www.ijcaonline.org/archives/volume154/number9/haque-2016-ijca-912220.pdf

Line Segmentation and Orientation Algorithm for Automatic Bengali License Plate Localization and Recognition

Microcalcifications (MC) are small deposits of calcium, which are associated with early signs of breast cancer. In this paper, a novel approach is presented to develop a computer-aided diagnosis (CADx) system for automatic differentiation between benign and malignant MC clusters based on their morphology, texture, and the distribution of individual and global features using an ensemble classifier. The images were enhanced, segmented and the feature extraction and selection phase were carried out to generate the feature space which was later fed into an ensemble classifier to classify the MC clusters. The validity of the proposed method was investigated by using two well-known digitized datasets that contain biopsy proven results for MC clusters: MIAS (24 images: 12 benign, 12 malignant) and DDSM (280 images: 148 benign and 132 malignant). A high classification accuracies (100% for MIAS and 91.39% for DDSM) and good ROC results (area under the ROC curve equal to 1 for MIAS and 0.91 for DDSM) were achieved. A full comparison with related publications is provided. The results indicate that the proposed approach is outperforming the current state-of-the-art methods.

Nashid Alam

Papers

A Segmentation based Automated System for Brain Tumor Detection

Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier.

Automatic Segmentation of Microcalcification Clusters

Line Segmentation and Orientation Algorithm for Automatic Bengali License Plate Localization and Recognition

Automatic classification of clustered microcalcifications in digitized mammogram using ensemble learning