Morphological Feature Extraction Research Articles

Morphological feature extraction and KNG‐CNN classification of CT images for early lung cancer detection

AbstractLung cancer is a dangerous disease causing death to individuals. Currently precise classification and differential diagnosis of lung cancer is essential with the stability and accuracy of cancer identification is challenging. Classification scheme was developed for lung cancer in CT images by Kernel based Non‐Gaussian Convolutional Neural Network (KNG‐CNN). KNG‐CNN comprises of three convolutional, two fully connected and three pooling layers. Kernel based Non‐Gaussian computation is used for the diagnosis of false positive or error encountered in the work. Initially Lung Image Database Consortium image collection (LIDC‐IDRI) dataset is used for input images and a ROI based segmentation using efficient CLAHE technique is carried as preprocessing steps, enhancing images for better feature extraction. Morphological features are extracted after the segmentation process. Finally, KNG‐CNN method is used for effectual classification of tumour > 30mm. An accuracy of 87.3% was obtained using this technique. This method is effectual for classifying the lung cancer from the CT scanned image.

Feature selection for classification in Steady state visually evoked potentials (SSVEP)-based brain-computer interfaces with genetic algorithm

Abstract Objectives Optimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy. Methods System of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI). Results The designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects. Conclusions It is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.

Plant species identification using color learning resources, shape, texture, through machine learning and artificial neural networks

Morphological characteristics are still the most used tools for the identification of plant species. In this context, leaves are the most available plant organ used, given their perenniality and diversity. Computer-based image analysis help extract morphological features for botanical identification and maybe a solution to taxonomic problems requiring extensively trained specialists that use visual identification as the primary method for this approach. In this study, were collected 40 leaves from 30 trees and shrub species from 19 different families. Here, we compared two popular image capture devices: a scanner and a mobile phone. Features analyzed comprised color, shape, and texture. The performance of both devices was compared through three machine learning algorithms (adaptive boosting—AdaBoost, random forest, support vector machine—SVM) and an artificial neural network model (deep learning). Computer vision showed to be efficient in the identification of species (higher than 93%), with similar results obtained for both mobile phones and scanners. The algorithms SVM, random forest and deep learning performed more efficiently than AdaBoost. Based on the results, we present the Inovtaxon Plant Species Identification Software, available at https://github.com/DeborahBambil/Inovtaxon .

A multistage deep neural network model for blood pressure estimation using photoplethysmogram signals

ObjectiveEasy access bio-signals are useful to alleviate the shortcomings and difficulties of cuff-based and invasive blood pressure (BP) measuring techniques. This study proposes a multistage model based on deep neural networks to estimate systolic and diastolic blood pressures using the photoplethysmogram (PPG) signal. MethodsThe proposed model consists of two key ingredients, using two successive stages. The first stage includes two convolutional neural networks (CNN) to extract morphological features from each PPG segment and then to estimate systolic and diastolic BPs separately. The second stage relies on long short-term memory (LSTM) to capture temporal dependencies. Further, the method incorporates the dynamic relationship between systolic and diastolic BPs to improve accuracy. ResultsThe proposed multistage model was evaluated on 200 subjects using the standards of the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). The results revealed that our model performance met the requirements of the AAMI standard. Also, according to the BHS standard, it achieved grade A in estimating both systolic and diastolic BPs. The mean and standard deviation of error for systolic and diastolic blood pressure estimations were +1.91±5.55mmHg and +0.67±2.84mmHg, respectively. ConclusionOur results highlight the benefits of the proposed model in terms of appropriate feature extraction as well as estimation consistency.

K-Means Color Segmentation and Morphological Feature Extraction of Bamboo Fiber as an Environmentally Friendly Material for Soil Strengthening

Indonesia is one of the bamboo producing countries. One of the characteristics of good bamboo is that bamboo has a good fiber content as well. Characteristics of a good bamboo fiber is fiber that has a size and length of fiber that is large and long because it will affect the compressive strength and tensile strength of the bamboo. This study uses the K-Means color segmentation method and shape measurements based on the morphological characteristics of bamboo fibers, namely the area, circumference / perimeter and fiber roundness ratio. The results of testing with 18 images of bamboo type training show that Wulung bamboo fiber which is still of the same genus with bamboo Gombong has the largest size fiber of 40.4, the longest perimeter of 3.73 and has a roundness ratio of 0.83 bamboo Ori which has an area of 21, 6, perimeter of 3.23 and a roundness ratio of 0.79. Bambu Petung has an area of 20.6, perimeter of 2.53 and roundness ratio of 0.79. Java Bamboo has an area of 20.2, perimeter of 1.19 and a roundness ratio of 0.8. Whereas Bambu Apus only has an area of 19.2, a perimeter of 2.09 and a roundness ratio of 0.78. Testing using 8 testing images obtained an accuracy rate of 0,625.

Pears Classification Using Principal Component Analysis and K-Nearest Neighbor

Pears is a fruit that is widely available in tropical climates such as in western Europe, Asia, Africa and one of them is Indonesia. There are many types of pears in Indonesia. Types of pears can be distinguished from the color, size, and shape. But it is still difficult for ordinary people to get to know the types of pears. This is what gave rise to the idea to conduct research related to image processing to classify three types of pears namely abate, red and william pears in order to help determine the type of pears. The pear type classification process is done by verify the image of pears based on existing training data. The research method used consisted of preprocessing image segmentation with morphological operations and feature extraction into Principal Component Analysis (PCA). The classification algorithm used is K-Nearest Neighbor (KNN). The use of adequate training data will further improve the classification of types of pears. The final results of this study amounted to 87.5%.

Rapid identification of chrysanthemum teas by computer vision and deep learning.

Seven commercial Chinese chrysanthemum tea products were classified by computer vision combined with machine learning algorithms. Without the need of building any specific hardware, the image acquisition was achieved in two computer vision approaches. In the first approach, a series of multivariate classification models were built after morphological feature extraction of the image. The best prediction accuracies when classifying flowering stages and tea types were respectively 90% and 63%. In comparison, the deep neural network was applied directly on the raw image, yielded 96% and 89% correct identifications when classifying flowering stage and tea type, respectively. The model can be applied for rapid and automatic quality determination of teas and other related foods. The result indicated that computer vision, especially when combined with deep learning or other machine learning techniques can be a convenient and versatile method in the evaluation of food quality.

NeuroPath2Path: Classification and elastic morphing between neuronal arbors using path-wise similarity.

Neuron shape and connectivity affect function. Modern imaging methods have proven successful at extracting morphological information. One potential path to achieve analysis of this morphology is through graph theory. Encoding by graphs enables the use of high throughput informatic methods to extract and infer brain function. However, the application of graph-theoretic methods to neuronal morphology comes with certain challenges in term of complex subgraph matching and the difficulty in computing intermediate shapes in between two imaged temporal samples. Here we report a novel, efficacious graph-theoretic method that rises to the challenges. The morphology of a neuron, which consists of its overall size, global shape, local branch patterns, and cell-specific biophysical properties, can vary significantly with the cell's identity, location, as well as developmental and physiological state. Various algorithms have been developed to customize shape based statistical and graph related features for quantitative analysis of neuromorphology, followed by the classification of neuron cell types using the features. Unlike the classical feature extraction based methods from imaged or 3D reconstructed neurons, we propose a model based on the rooted path decomposition from the soma to the dendrites of a neuron and extract morphological features from each constituent path. We hypothesize that measuring the distance between two neurons can be realized by minimizing the cost of continuously morphing the set of all rooted paths of one neuron to another. To validate this claim, we first establish the correspondence of paths between two neurons using a modified Munkres algorithm. Next, an elastic deformation framework that employs the square root velocity function is established to perform the continuous morphing, which, as an added benefit, provides an effective visualization tool. We experimentally show the efficacy of NeuroPath2Path, NeuroP2P, over the state of the art.

Imaging Wheat Canopy Through Stereo Vision: Overcoming the Challenges of the Laboratory to Field Transition for Morphological Features Extraction.

Stereo vision is a 3D imaging method that allows quick measurement of plant architecture. Historically, the method has mainly been developed in controlled conditions. This study identified several challenges to adapt the method to natural field conditions and propose solutions. The plant traits studied were leaf area, mean leaf angle, leaf angle distribution, and canopy height. The experiment took place in a winter wheat, Triticum aestivum L., field dedicated to fertilization trials at Gembloux (Belgium). Images were acquired thanks to two nadir cameras. A machine learning algorithm using RGB and HSV color spaces is proposed to perform soil-plant segmentation robust to light conditions. The matching between images of the two cameras and the leaf area computation was improved if the number of pixels in the image of a scene was binned from 2560 × 2048 to 1280 × 1024 pixels, for a distance of 1 m between the cameras and the canopy. Height descriptors such as median or 95th percentile of plant heights were useful to precisely compare the development of different canopies. Mean spike top height was measured with an accuracy of 97.1 %. The measurement of leaf area was affected by overlaps between leaves so that a calibration curve was necessary. The leaf area estimation presented a root mean square error (RMSE) of 0.37. The impact of wind on the variability of leaf area measurement was inferior to 3% except at the stem elongation stage. Mean leaf angles ranging from 53° to 62° were computed for the whole growing season. For each acquisition date during the vegetative stages, the variability of mean angle measurement was inferior to 1.5% which underpins that the method is precise.

Automated Cardiovascular Pathology Assessment Using Semantic Segmentation and Ensemble Learning.

Cardiac magnetic resonance imaging provides high spatial resolution, enabling improved extraction of important functional and morphological features for cardiovascular disease staging. Segmentation of ventricular cavities and myocardium in cardiac cine sequencing provides a basis to quantify cardiac measures such as ejection fraction. A method is presented that curtails the expense and observer bias of manual cardiac evaluation by combining semantic segmentation and disease classification into a fully automatic processing pipeline. The initial processing element consists of a robust dilated convolutional neural network architecture for voxel-wise segmentation of the myocardium and ventricular cavities. The resulting comprehensive volumetric feature matrix captures diagnostic clinical procedure data and is utilized by the final processing element to model a cardiac pathology classifier. Our approach evaluated anonymized cardiac images from a training data set of 100 patients (4 pathology groups, 1 healthy group, 20 patients per group) examined at the University Hospital of Dijon. The top average Dice index scores achieved were 0.940, 0.886, and 0.849 for structure segmentation of the left ventricle (LV), myocardium, and right ventricle (RV), respectively. A 5-ary pathology classification accuracy of 90% was recorded on an independent test set using the trained model. Performance results demonstrate the potential for advanced machine learning methods to deliver accurate, efficient, and reproducible cardiac pathological assessment.

Automated Analysis of Ultrasound Videos for Detection of Breast Lesions

Background: Breast cancer is the second cause of death among women. Ultrasound (US) imaging is the most common technique for diagnosing breast cancer; however, detecting breast lesions in US images is a difficult task, mainly, because it provides low-quality images. Consequently, identifying lesions in US images is still a challenging task and an open problem in US image processing. This study aims to develop an automated system for the identification of lesions in US images Method: We proposed an automatic method to assist radiologists in inspecting and analyzing US images in breast screening and diagnosing breast cancer. In contrast to previous research, this work focuses on fusing information extracted from different frames. The developed method consists of template matching, morphological features extraction, local binary patterns, fuzzy C-means clustering, region growing, and information fusion-based image segmentation technique. The performance of the system was evaluated using a database composed of 22 US videos where 10 breast US films were obtained from patients with breast lesions and 12 videos belonged to normal cases. Results: The sensitivity, specificity, and accuracy of the system in detecting frames with breast lesions were 95.7%, 97.1%, and 97.1%, respectively. The algorithm reduced the vibration of the physician’s hands’ while probing by assessing every 10 frames regardless of the results of the prior frame; hence, lowering the possibility of missing a lesion during an examination. Conclusion: The presented system outperforms several existing methods in correctly detecting breast lesions in a breast cancer screening test. Fusing information that exists in frames of a breast US film can help improve the identification of lesions (suspect regions) in a screening test.

Automatic Recognition of Palaeobios Images Under Microscope Based on Machine Learning

The research of paleontology is an essential part of contemporary earth science. However, the time-consuming manual identification process has always been cumbrous in the field of paleontology. Since conventional algorithms have limited efficiency in processing images of complicated paleontological fossils. In this work, a combinational machine learning method, which comprises appropriate image preprocessing, Scale-invariant feature transform (SIFT), K-means clustering (K-means), and Support Vector Machine (SVM) are applied to realize automatic recognition of paleontological images under microscope. It is demonstrated that this combined algorithm has superior performance in morphological feature extraction in the case of complex mineral textures. With this technique, the overall average accuracy of image recognition is 84.5%, which significantly improved the efficiency of sample analysis in the field of paleontology.

Quantification of Morphological Features in Non-Contrast-Enhanced Ultrasound Microvasculature Imaging.

There are significant differences in microvascular morphological features in diseased tissues, such as cancerous lesions, compared to noncancerous tissue. Quantification of microvessel morphological features could play an important role in disease diagnosis and tumor classification. However, analyzing microvessel morphology in ultrasound Doppler is a challenging task due to limitations associated with this technique. Our main objective is to provide methods for quantifying morphological features of microvasculature obtained by ultrasound Doppler imaging. To achieve this goal, we propose multiple image enhancement techniques and appropriate morphological feature extraction methods that enable quantitative analysis of microvasculature structures. Vessel segments obtained by the skeletonization of the regularized microvasculature images are further analyzed to satisfy other constraints, such as vessel segment diameter and length. Measurements of some morphological metrics, such as tortuosity, depend on preserving large vessel trunks. To address this issue, additional filtering methods are proposed. These methods are tested on in vivo images of breast lesion and thyroid nodule microvasculature, and the outcomes are discussed. Initial results show that using vessel morphological features allows for differentiation between malignant and benign breast lesions (p-value < 0.005) and thyroid nodules (p-value < 0.01). This paper provides a tool for the quantification of microvasculature images obtained by non-contrast ultrasound imaging, which may serve as potential biomarkers for the diagnosis of some diseases.

浮选气泡NSCT域多尺度等效形态特征提取及识别

浮选气泡NSCT域多尺度等效形态特征提取及识别

SEPARATION AND CLASSIFICATION OF FETAL ECG SIGNAL BY ENHANCED BLIND SOURCE SEPARATION TECHNIQUE AND NEURAL NETWORK

The separation of Fetal ElectroCardioGram (FECG) from mother's Abdominal ECG (AECG) signal is complicated and very important in medical diagnosis during pregnancy. In this study, the separation and classification of FECG signal from AECG signals is presented by a novel method. It uses MULTI-COMBI based Blind Source Separation (BSS) technique to separate the FECG signals. The separated FECG signals have different features, which are extracted using the morphological feature extraction method. These extracted features are used for classification by using Feed Forward Neural Network (FFNN). This classifier classifies the FECG into five different classes. The entire work is implemented in MATLAB. Results show that FFNN gives the classification accuracy of 77.1%, sensitivity of 75.3%, and specificity of 76.7%.

Feature Extraction for Hyperspectral Images Using Local Contain Profile

Spectral-spatial information extraction is always important for hyperspectral image analysis, such as classification and detection. Extinction profile (EP), based on component tree (max-tree/min-tree), has been recently-proposed as one of the best morphological feature extraction methods. As an alternative, a new local contain profile (LCP), employing topology tree in the tree generation process, has been proposed. Topology tree, including the tree of shapes and the inclusion tree, is constructed by the inclusion relationship between the connected components belonging to the same level in the image. Furthermore, several new morphological properties, such as compactness, and elongation, are designed to accurately exploit specific shape information. The proposed LCP is expected to discard some irrelevant spatial information while preserving useful spatial characteristics. Experimental results validated on several real hyperspectral data demonstrate that the proposed LCP can significantly improve accuracy and decrease the half of feature dimension when compared to the state-of-the-art EP.

A Framework for Automatic Morphological Feature Extraction and Analysis of Abdominal Organs in MRI Volumes

The accurate 3D reconstruction of organs from radiological scans is an essential tool in computer-aided diagnosis (CADx) and plays a critical role in clinical, biomedical and forensic science research. The structure and shape of the organ, combined with morphological measurements such as volume and curvature, can provide significant guidance towards establishing progression or severity of a condition, and thus support improved diagnosis and therapy planning. Furthermore, the classification and stratification of organ abnormalities aim to explore and investigate organ deformations following injury, trauma and illness. This paper presents a framework for automatic morphological feature extraction in computer-aided 3D organ reconstructions following organ segmentation in 3D radiological scans. Two different magnetic resonance imaging (MRI) datasets are evaluated. Using the MRI scans of 85 adult volunteers, the overall mean volume for the pancreas organ is 69.30 ± 32.50cm3, and the 3D global curvature is (35.23 ± 6.83) × 10−3. Another experiment evaluates the MRI scans of 30 volunteers, and achieves mean liver volume of 1547.48 ± 204.19cm3 and 3D global curvature (19.87 ± 3.62) × 10− 3. Both experiments highlight a negative correlation between 3D curvature and volume with a statistical difference (p < 0.0001). Such a tool can support the investigation into organ related conditions such as obesity, type 2 diabetes mellitus and liver disease.

A real-time approach for automatic defect detection from PCBs based on SURF features and morphological operations

This paper presents an automatic inspection approach for Printed Circuit Boards (PCBs) with accurate determination of the fault location and identification of the fault type. This approach depends on several digital image processing techniques including registration, filtering, foreground segmentation, mathematical morphological operations, subtraction, feature extraction, and component matching. The Speeded Up Robust Feature extraction (SURF) technique is used for two purposes: registration of the PCB to be checked to a reference PCB and detection of feature points of each missing component on the PCB that is localized from the subtraction process from the reference PCB. Operation is performed on the hue component of the color PCB images. A dictionary is first built for all possible components on the available PCBs with SURF feature descriptors, and hence if a missing item is detected on a PCB during the inspection process, the SURF feature descriptors for features extracted from the difference between the tested and reference PCBs at the position of the lost component are matched with those in the built dictionary or database. A distance metric is used in the matching process. The importance of the proposed approach lies in its ability to build a dictionary of feature descriptors for all possible components in a diversity of PCBs and its ability to localize and identify the missing components regardless of the PCB position, rotation, or type. All operations are formulated in a Graphical User Interface (GUI) using MATLAB environment.

Evaluation of Acridine Orange Staining for a Semi-Automated Urinalysis Microscopic Examination at the Point-of-Care.

A urinary tract infection (UTI) can be diagnosed via urinalysis, consisting of a dipstick test and manual microscopic examination. Point-of-care (POC) image-based systems have been designed to automate the microscopic examination for low-volume laboratories or low-resource clinics. In this pilot study, acridine orange (AO) was evaluated as a fluorescence-based contrast agent to aid in detecting and enumerating urine sediment specific for diagnosing a UTI. Acridine orange staining of epithelial cells, leukocytes, and bacteria provided sufficient contrast to successfully implement image segmentation techniques, which enabled the extraction of classifiable morphologic features. Surface area bounded by each cell border was used to differentiate the sediment; epithelial cells were larger than 500μm2, bacteria were less than 30μm2, and leukocytes in between. This image-based semi-automated technique using AO resulted in similar cell counts to the clinical results, which demonstrates the feasibility of AO as an aid for POC urinalysis systems.

Morphological Brain Age Prediction using Multi-View Brain Networks Derived from Cortical Morphology in Healthy and Disordered Participants

Brain development and aging are dynamic processes that unfold over years on multiple levels in both healthy and disordered individuals. Recent studies have revealed a disparity between the chronological brain age and the ‘data-driven’ brain age using functional MRI (fMRI) and diffusion MRI (dMRI). Particularly, predicting the ‘brain age’ from connectomic data might help identify relevant connectional biomarkers of neurological disorders that emerge early or late in the lifespan. While prior brain-age prediction studies have relied exclusively on either structural or functional connectomic data, here we unprecedentedly propose to predict the morphological age of the brain by solely using morphological brain networks (derived from T1-weighted images) in both healthy and disordered populations. Besides, although T1-weighted MRI was widely used for brain age prediction, it was leveraged from an image-based analysis perspective not from a connectomic perspective. Our method includes the following steps: (i) building multi-view morphological brain networks (M-MBN), (ii) feature extraction and selection, (iii) training a machine-learning regression model to predict age from M-MBN data, and (iv) utilizing our model to identify connectional brain features related to age in both autistic and healthy populations. We demonstrate that our method significantly outperforms existing approaches and discovered brain connectional morphological features that fingerprint the age of brain cortical morphology in both autistic and healthy individuals. In particular, we discovered that the connectional cortical thickness best predicts the morphological age of the autistic brain.