Image Segmentation Step Research Articles

AIBH: Accurate Identification of Brain Hemorrhage Using Genetic Algorithm Based Feature Selection and Stacking

Brain hemorrhage is a type of stroke which is caused by a ruptured artery, resulting in localized bleeding in or around the brain tissues. Among a variety of imaging tests, a computerized tomography (CT) scan of the brain enables the accurate detection and diagnosis of a brain hemorrhage. In this work, we developed a practical approach to detect the existence and type of brain hemorrhage in a CT scan image of the brain, called Accurate Identification of Brain Hemorrhage, abbreviated as AIBH. The steps of the proposed method consist of image preprocessing, image segmentation, feature extraction, feature selection, and design of an advanced classification framework. The image preprocessing and segmentation steps involve removing the skull region from the image and finding out the region of interest (ROI) using Otsu’s method, respectively. Subsequently, feature extraction includes the collection of a comprehensive set of features from the ROI, such as the size of the ROI, centroid of the ROI, perimeter of the ROI, the distance between the ROI and the skull, and more. Furthermore, a genetic algorithm (GA)-based feature selection algorithm is utilized to select relevant features for improved performance. These features are then used to train the stacking-based machine learning framework to predict different types of a brain hemorrhage. Finally, the evaluation results indicate that the proposed predictor achieves a 10-fold cross-validation (CV) accuracy (ACC), precision (PR), Recall, F1-score, and Matthews correlation coefficient (MCC) of 99.5%, 99%, 98.9%, 0.989, and 0.986, respectively, on the benchmark CT scan dataset. While comparing AIBH with the existing state-of-the-art classification method of the brain hemorrhage type, AIBH provides an improvement of 7.03%, 7.27%, and 7.38% based on PR, Recall, and F1-score, respectively. Therefore, the proposed approach considerably outperforms the existing brain hemorrhage classification approach and can be useful for the effective prediction of brain hemorrhage types from CT scan images (The code and data can be found here: http://cs.uno.edu/~tamjid/Software/AIBH/code_data.zip).

A Novel Method to Detect and Classify Oral Cavity Cancer System

The primary objective of this article is to build a Computer Aided Diagnostic (CAD) technique for the segmentation of Oral cavity cancer. The frequency of oral cancer occurrence has been observed globally in recent decades. The death ratio from oral cancers is excessive and keeps on rising. This article has concentrated on oral Computed Tomography (CT) image pre-processing and segmentation techniques to enhance resolution of the image and readability to enhance classification outcome. The proposed model targeted on image pre-processing and segmentation steps to improve the resolution of the image, then enhance the precision of swelling recognition and classification. The American Cancer Society calculates that there will be almost 60,000 new patients and roughly 9,000 deaths from oral or throat cancer in 2017. The preliminary prediction of oral cancer is to check out the ocular areas cautiously and record the holes present in the teeth of the damaged person as true-color digital images. The choice about the extra cure of the oral cancer damaged person is principally based on the growth of the lesion. Oral cancers are a type of cancer, the place few irregular wounds or patches will present in the holes present in the teeth. As it is challenging to recognize it in the beginning states, it has one of the bad survival ratios. The suggested method receives the Computerized Tomography (CT) scanned images of the cancer damaged vicinity and can detect the presence of malignancy. The managing of oral cancer is a multidisciplinary attempt, as every damaged person provides the treating clinicians with a sole collection of difficulties, the managing of which affects on each living and high- condition of life.

PolSAR image segmentation based on feature extraction and data compression using Weighted Neighborhood Filter Bank and Hidden Markov random field-expectation maximization

Despite the good performance of methods such as wavelet transform, Gabor filter bank, and scale-invariant feature transform (SIFT) as the first step of image segmentation, they have been some difficulties in the selection of suitable numbers of scales and directions for them. In this paper, a novel method is presented for Polarimetric Synthetic Aperture Radar (PolSAR) image segmentation, in which there is no need for any parameter initialization. The proposed method, called POLWHE is based on Lossy Minimum Descriptive Length (LMDL), Zero Padding Weighted Neighborhood Filter Bank (ZPWNFB) and Hidden Markov Random Field-Expectation Maximization (HMRF-EM). In the proposed method, first, Simple Linear Iterative Clustering (SLIC) and entropy are used for the adaptive generation of superpixels. Then, these superpixels are merged based on the learned texture and color features and the ZPWNFB edge-map. This procedure results in the image being segmented automatically. Next, the initial labels of the image are assigned by k-means and improved by HMRF-EM and ZPWNFB (KWHE). Finally, the automatic method and KWHE are combined and the final segmentation result is obtained. The proposed method is tested on real PolSAR images. The results prove the superiority of the proposed method as it improves both the performance and the noise resistance with the mean accuracy of 92.30%.

Multi-Feature Weighted Sparse Graph for SAR Image Analysis

Sparse representation (SR) method has the advantages of good category distinguishing performance, noise robustness, and data adaptiveness. In this article, a multi-feature weighted sparse graph (MWSG) is presented for synthetic aperture radar (SAR) image analysis. First, multiple types of features are extracted to fully describe the characteristics of SAR image. Then, multiple SRs of samples in multiple feature spaces are obtained by solving a weighted joint SR model, in which the weight is the Gaussian kernel distance among samples. Moreover, a new fusion mechanism is given to integrate multiple weighted SRs, which aims to eliminate the negative influence of the singular data, so the MWSG is obtained. Afterward, the brief steps of the SAR image segmentation and semisupervised classification based on MWSG are stated. A series of experiments on the simulated and real SAR images shows that the MWSG has better performance than other existing relevant methods.

Segmentation of dental cone-beam CT scans affected by metal artifacts using a mixed-scale dense convolutional neural network.

PurposeIn order to attain anatomical models, surgical guides and implants for computer‐assisted surgery, accurate segmentation of bony structures in cone‐beam computed tomography (CBCT) scans is required. However, this image segmentation step is often impeded by metal artifacts. Therefore, this study aimed to develop a mixed‐scale dense convolutional neural network (MS‐D network) for bone segmentation in CBCT scans affected by metal artifacts.MethodTraining data were acquired from 20 dental CBCT scans affected by metal artifacts. An experienced medical engineer segmented the bony structures in all CBCT scans using global thresholding and manually removed all remaining noise and metal artifacts. The resulting gold standard segmentations were used to train an MS‐D network comprising 100 convolutional layers using far fewer trainable parameters than alternative convolutional neural network (CNN) architectures. The bone segmentation performance of the MS‐D network was evaluated using a leave‐2‐out scheme and compared with a clinical snake evolution algorithm and two state‐of‐the‐art CNN architectures (U‐Net and ResNet). All segmented CBCT scans were subsequently converted into standard tessellation language (STL) models and geometrically compared with the gold standard.ResultsCBCT scans segmented using the MS‐D network, U‐Net, ResNet and the snake evolution algorithm demonstrated mean Dice similarity coefficients of 0.87 ± 0.06, 0.87 ± 0.07, 0.86 ± 0.05, and 0.78 ± 0.07, respectively. The STL models acquired using the MS‐D network, U‐Net, ResNet and the snake evolution algorithm demonstrated mean absolute deviations of 0.44 mm ± 0.13 mm, 0.43 mm ± 0.16 mm, 0.40 mm ± 0.12 mm and 0.57 mm ± 0.22 mm, respectively. In contrast to the MS‐D network, the ResNet introduced wave‐like artifacts in the STL models, whereas the U‐Net incorrectly labeled background voxels as bone around the vertebrae in 4 of the 9 CBCT scans containing vertebrae.ConclusionThe MS‐D network was able to accurately segment bony structures in CBCT scans affected by metal artifacts.

Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis.

Radiomics in nuclear medicine is rapidly expanding. Reproducibility of radiomics studies in multicentre settings is an important criterion for clinical translation. We therefore performed a meta-analysis to investigate reproducibility of radiomics biomarkers in PET imaging and to obtain quantitative information regarding their sensitivity to variations in various imaging and radiomics-related factors as well as their inherent sensitivity. Additionally, we identify and describe data analysis pitfalls that affect the reproducibility and generalizability of radiomics studies. After a systematic literature search, 42 studies were included in the qualitative synthesis, and data from 21 were used for the quantitative meta-analysis. Data concerning measurement agreement and reliability were collected for 21 of 38 different factors associated with image acquisition, reconstruction, segmentation and radiomics-specific processing steps. Variations in voxel size, segmentation and several reconstruction parameters strongly affected reproducibility, but the level of evidence remained weak. Based on the meta-analysis, we also assessed inherent sensitivity to variations of 110 PET image biomarkers. SUVmean and SUVmax were found to be reliable, whereas image biomarkers based on the neighbourhood grey tone difference matrix and most biomarkers based on the size zone matrix were found to be highly sensitive to variations, and should be used with care in multicentre settings. Lastly, we identify 11 data analysis pitfalls. These pitfalls concern model validation and information leakage during model development, but also relate to reporting and the software used for data analysis. Avoiding such pitfalls is essential for minimizing bias in the results and to enable reproduction and validation of radiomics studies.

Automatic wrinkles detection on face image

Image processing techniques are beneficial for reconstructing and processing an image in resulting different images. One of its benefits is it can be used for detecting wrinkles on a face. Line characteristic of wrinkles can be detected also with the support of the features that had been provided. The purpose of this research is to design a system that could detect the exact location of the line of wrinkles which occur from one face image. Generally, the step that required for this research is the HSV colour image segmentation step and K-Means Clustering for dividing skin object and non-skin object. The second step is phase image improvement step with Laplacian of Gaussian filter to sharpening line of wrinkles on image face, and the last step is feature extraction which used for taking wrinkles. After all the steps had been done, each of the wrinkle’s areas are detected with Canny filter and eliminated based on Area characteristic. From research experiment, the result validation data of system based on an expert are 40.6% for sensitivity, 96.9% for specificity, and 94.2% for accuracy, with the performance of the system 5,247 seconds.

Automated analysis of visual leaf shape features for plant classification

A large number of studies have been performed during the past few years to automatically identify the plant type in a given image. Besides common object recognition difficulties arising mainly due to light, pose and orientation variations, the plant type identification problem is further complicated by the differences in leaf shape overage and changing leaf color under different weather conditions. The limited accuracy of existing approaches can be improved using an appropriate selection of representative leaf based features. This study evaluates different handcrafted visual leaf features, their extraction techniques, and classification methods. Towards this end, a new five-step algorithm is presented (comprising image pre-processing, segmentation, feature extraction, dimensionality reduction, and classification steps) for recognition of plant type through leaf images. The proposed algorithm is evaluated on a publicly available standard dataset ‘Flavia’ of 1600 leaf images and on a self-collected dataset of 625 leaf images. With the proposed algorithm, different classifiers such as k-nearest neighbor (KNN), decision tree, naïve Bayes, and multi-support vector machines (SVM) are tested. The best performing KNN, claimed for the final results, reveals that the proposed algorithm gives precision and recall values of 97.6% and 98.8% respectively when tested on ‘Flavia’ dataset. The proposed technique is also tested on our self-collected dataset, giving respectively 96.1% and 97.3% precision and recall measure results. Results confirm that our approach, when augmented with efficient segmentation techniques on raw leaf images, can be a significantly accurate plant type recognition method in practical situations. AlexNet, a Convolutional Neural Network (CNN) based approach is also compared for classification on the datasets as oppose to handcrafted feature-based approach and it is found that the later outperforms the former in robustness when the training dataset is small.

A smartphone-based apple yield estimation application using imaging features and the ANN method in mature period

Apple yield estimation using a smartphone with image processing technology offers advantages such as low cost, quick access and simple operation. This article proposes distribution framework consisting of the acquisition of fruit tree images, yield prediction in smarphone client, data processing and model calculation in server client for estimating the potential fruit yield. An image processing method was designed including the core steps of image segmentation with R/B value combined with V value and circle-fitting using curvature analysis. This method enabled four parameters to be obtained, namely, total identified pixel area (TP), fitting circle amount (FC), average radius of the fitting circle (RC) and small polygon pixel area (SP). A individual tree yield estimation model on an ANN (Artificial Neural Network) was developed with three layers, four input parameters, 14 hidden neurons, and one output parameter. The system was used on an experimental Fuji apple (Malus domestica Borkh. cv. Red Fuji) orchard. Twenty-six tree samples were selected from a total of 80 trees according to the multiples of the number three for the establishment model, whereby 21 groups of data were trained and 5 groups o data were validated. The R2 value for the training datasets was 0.996 and the relative root mean squared error (RRMSE) value 0.063. The RRMSE value for the validation dataset was 0.284 Furthermore, a yield map with 80 apple trees was generated, and the space distribution o the yield was identified. It provided appreciable decision support for site-specific management.

Computational Deep Intelligence Vision Sensing for Nutrient Content Estimation in Agricultural Automation

This paper presents a novel computational intelligence vision sensing approach to estimate nutrient content in wheat leaves by analyzing color features of the leaves images captured on field with various lighting conditions. We propose the development of deep sparse extreme learning machines (DSELM) fusion and genetic algorithm (GA) to normalize plant images as well as to reduce color variability due to a variation of sunlight intensities. We also apply the DSELM in image segmentation to differentiate wheat leaves from a complex background. In this paper, four moments of color distribution of the leaves images (mean, variance, skewness, and kurtosis) are extracted and utilized as predictors in the nutrient estimation. We combine a number of DSELMs with committee machine and optimize them using the GA to estimate nitrogen content in wheat leaves. The results have shown the superiority of the proposed method in the term of quality and processing speed in all steps, i.e., color normalization, image segmentation, and nutrient prediction, as compared with other existing methods. Note to Practitioners —In order to support precision farming technology and agricultural automation, it is very essential to estimate the nutrient content in plants to prevent over fertilizing that will harm environment. Furthermore, the existing method to determine nutrient content in leaves is destructive as well as time consuming and requires special expertise to operate expensive devices. A number of methods have been developed to estimate nutrient content in leaves nondestructively based on the color features of the leaves. Most of these methods, however, are conducted in a controlled environment with artificial lighting. Such methods are not practical and need various equipment. We propose a low-cost, simple, and accurate technique to estimate nitrogen content in wheat leaves by analyzing RGB color of the leaves images. In this paper, we found that the developed DSELMs fusion has enabled better performance in normalizing images and is faster than other neural network types, i.e., backpropagation-based multilayer perceptron and original extreme learning machine. In image segmentation step, the established DSELM shows good performance to recognize and distinguish wheat leaves from other undesired background images. Furthermore, the developed weighted DSELMs have demonstrated enhanced ability in estimating nutrient content.

Content-aware detection of JPEG grid inconsistencies for intuitive image forensics

The paper proposes a novel method for detecting indicators of image forgery by locating grid alignment abnormalities in JPEG compressed image bitmaps. The method evaluates multiple grid positions with respect to a fitting function, and areas of lower contribution are identified as grid discontinuities and possibly tampered areas. An image segmentation step is introduced to differentiate between discontinuities produced by tampering and those that are attributed to image content, making the output maps easier to interpret by suppressing non-relevant activations. Our evaluations, on both synthetically produced datasets and real world tampering cases against seven methods from the literature, highlight the effectiveness of the proposed method in its ability to produce output maps that are clear and readable, and which can achieve successful detections on cases where other algorithms fail.

Robust edge detection based on Modified Moore-Neighbor

Edge detection is a fundamental step in image segmentation and one of the most important steps to recognize the features of objects. In this paper, we proposed an edge detection method using modified Moore-Neighbor algorithm. The Modified Moore-neighbor algorithm is used as boundary tracing for detection and feature extraction. Further, range filter is incorporated with modified Moore-Neighbor to detect the edges of objects. The proposed edge detection can extract the true edges as well as features of an object and is robust to noise. We have considered both objective evaluation and subjective evaluation to verify the results. The image analysis parameters are considered for subjective evaluation. Further, a total number of 24 BSD (Berkeley Segmentation Database) images are considered for subjective evaluation using the proposed method and compared with the other existing methods. The results obtained using the proposed modified Moore-Neighbor algorithm for different test images and BSD images are found to be promising. Therefore, proposed edge detection method warrants further investigation.

Early detection of joint abnormalities from ultrasound images

Musculoskeletal ultrasound is effective for the early detection of joint abnormalities like erosion, effusion, synovitis and inflammation. Computer software is developed for segmentation of joint ultrasound image to diagnose the defect. The objective of developing this paper is to achieve early diagnosis of joint disorders by segmentation of ultrasound image with different algorithms. Ultrasound machine with high resolution probe can be used for development & findings of joints by the orthopaedician, rheumatologist and sports physician. These find-ings are done by processing the ultrasound images of patient joint using modern image processing techniques. Therefore algorithms has been designed and developed for analysis of medical images that is musculo ultrasound image based on optimization approach, using genet-ic algorithm and PSO algorithm. To improve the better quality of the image many improvisation techniques have been introduced. Hence, these algorithms perform better segmentation and identification of joint abnormalities. The analysis of ultrasound image is directly based on image segmentation steps, pre-processing, filtering, feature extraction and analysis of these extracted features by finding the output using different optimization techniques. In proposed method, efforts have been made to exhibit the procedure for finding and segmenting the mus-culoskeletal images of abnormal joints. The present approaches are segmentation operation on ultrasound images by applying genetic and PSO algorithm. The comparison between these algorithms is done, such that the algorithm itself analyses the whole image and perform the segmentation and detection of abnormalities perfectly

An image segmentation based algorithm for imaging of slow slip earthquakes

Laboratory experiments next to a variety of observations, especially in subduction zones, have explored the existence of a premonitory stable slow slip growth phase preceding large earthquakes. These phenomena play an important role in the earthquake cycle and thus precise imaging and monitoring of these events are of great significance. In the literature, ENIF (extended network inversion filter) has been proposed as a rigorous algorithm capable of isolating signal from different types of noise and thereby provides us with deep insight into spatio-temporal evolution of slow slip events. Despite its considerable advantages, ENIF still suffers from some limitations. ENIF applies Tikhonov method of regularization with a quadratic form of cost function. While anomalous slip regions have clear contrast with the background slip in reality, Tikhonov regularization tends to over smooth (globally smooth) the slipping portion on the estimated images. In order to avoid over smoothing phenomenon, we have incorporated into ENIF an image segmentation step which tries to preserve edges of slow-slip event. As a second limitation, due to the nonlinearity imposed by such constraint as non-negativity of slip rate, uncertainty propagation through model is not simple. As the core of ENIF, EKF (extended Kalman filter), performs uncertainty propagation by linearization of nonlinear model using Jacobian and Hessian matrices. As an alternative for EKF, we have also investigated the application of UKF (unscented Kalman filter) which uses UT (unscented transform) for uncertainty propagation. Finally, we tested our proposed algorithm using a low signal to noise ratio synthetic data set. The results show a significant improvement in the performance of ENIF when the segmentation step is incorporated into the algorithm.

Image smoothing via truncated gradient regularisation

Edge-preserving image smoothing aims at maintaining the fundamental constituents, i.e. salient edges of a given image, while removing the noise and insignificant details in the meantime. It is often employed at the pre-processing step of many image processing tasks, including reconstruction, segmentation, recognition and three-dimensional content generation, to name just a few. Recently, a sparse gradient counting scheme in an optimisation framework has attracted much attention, and it confines the discrete number of intensity changes among neighbouring pixels. This l 0 -regularised sparsity pursuit scheme performs favourably in a global optimisation manner. However, it often achieves unsatisfactory performance at diminishing trivial details and at smoothing discrete regions. In this study, a new image smoothing scheme with truncated l 0 regularisation is proposed, which is especially effective for sharpening critical edges. For objective evaluation of the smoothing performance, images are linearly quantised into several layers to generate the experimental images, then these quantised images are smoothed using several methods for reconstructing the smoothly changed shape and intensity of the original images. Compared with the l 0 smoothing scheme, extensive experimental results demonstrate that the proposed method performs much better at preserving main structures and removing trivial details.

Inhomogeneity correction and hybrid-based segmentation in cardiac MRI

Image segmentation is an important step in medical image analysis and segmentation of ventricles in Cardiac Magnetic Resonance (CMR) images is challenging due to an in-built artefact called intensity-inhomogeneity. The short-axis cine Magnetic Resonance Images (MRI) recorded under a steady-state free precision protocol were corrected for intensity-inhomogeneity using Bias Corrected Fuzzy C-Means (BCFCM) method, Level Set (LS) and Multiplicative Intrinsic Component Optimisation (MICO) methods. The statistical measures show that bias correction by BCFCM has better performance than MICO and LS. In addition, the original and bias corrected images are validated by Multifractal Analysis (MFA). The results show that in bias corrected images, the low frequency components are removed thereby enhancing the sharpness of the ventricular boundaries. Further, ventricular segmentation is performed using the proposed automatic hybrid Sobel edge detector with optimised level set method. The validation parameters of segmented results show that the ventricular detection in bias corrected images matches better with ground truth.

A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection

Edge detection in medical imaging is a significant task for object recognition of human organs and it is considered a pre-processing step in medical image segmentation and reconstruction. This article proposes an efficient approach based on generalized Hill entropy to find a good solution for detecting edges under noisy conditions in medical images. The proposed algorithm uses a two-phase thresholding: firstly, a global threshold is calculated by means of generalized Hill entropy and used to separate the image into object and background. Afterwards, a local threshold value is determined for each part of the image. The final edge map image is a combination of these two separate images based on the three calculated thresholds. The performance of the proposed algorithm is compared against Canny and Tsallis entropy by using sets of medical images corrupted with various types of noise. We used Pratt’s Figure of Merit (PFOM) as a quantitative measure for an objective comparison. Experimental results indicate that the proposed algorithm displayed superior noise resilience and better edge detection than Canny and Tsallis entropy methods for the four different types of noise analyzed, and thus it can be considered as a very interesting edge detection algorithm on noisy medical images.

A fourth order PDE based fuzzy c- means approach for segmentation of microscopic biopsy images in presence of Poisson noise for cancer detection

Background and objectiveFor cancer detection from microscopic biopsy images, image segmentation step used for segmentation of cells and nuclei play an important role. Accuracy of segmentation approach dominate the final results. Also the microscopic biopsy images have intrinsic Poisson noise and if it is present in the image the segmentation results may not be accurate. The objective is to propose an efficient fuzzy c-means based segmentation approach which can also handle the noise present in the image during the segmentation process itself i.e. noise removal and segmentation is combined in one step. MethodsTo address the above issues, in this paper a fourth order partial differential equation (FPDE) based nonlinear filter adapted to Poisson noise with fuzzy c-means segmentation method is proposed. This approach is capable of effectively handling the segmentation problem of blocky artifacts while achieving good tradeoff between Poisson noise removals and edge preservation of the microscopic biopsy images during segmentation process for cancer detection from cells. ResultsThe proposed approach is tested on breast cancer microscopic biopsy data set with region of interest (ROI) segmented ground truth images. The microscopic biopsy data set contains 31 benign and 27 malignant images of size 896 × 768. The region of interest selected ground truth of all 58 images are also available for this data set. Finally, the result obtained from proposed approach is compared with the results of popular segmentation algorithms; fuzzy c-means, color k-means, texture based segmentation, and total variation fuzzy c-means approaches. ConclusionsThe experimental results shows that proposed approach is providing better results in terms of various performance measures such as Jaccard coefficient, dice index, Tanimoto coefficient, area under curve, accuracy, true positive rate, true negative rate, false positive rate, false negative rate, random index, global consistency error, and variance of information as compared to other segmentation approaches used for cancer detection.

ELECTRON ABSORBED FRACTIONS IN AN IMAGE-BASED MICROSCOPIC SKELETAL DOSIMETRY MODEL OF CHINESE ADULT MALE.

Based on the Chinese reference adult male voxel model, a set of microscopic skeletal models of Chinese adult male is constructed through the processes of computed tomography (CT) imaging, bone coring, micro-CT imaging, image segmentation, merging into macroscopic bone model and implementation in Geant4. At the step of image segmentation, a new bone endosteum (BE) segmentation method is realized by sampling. The set of model contains 32 spongiosa samples with voxel size of 19 μm cubes. The microscopic spongiosa bone data for Chinese adult male are provided. Electron absorbed fractions in red bone marrow (RBM) and BE are calculated. Source tissues include the bone marrow (red and yellow), trabecular bone (surfaces and volumes) and cortical bone (surfaces and volumes). Target tissues include RBM and BE. Electron energies range from 10 keV to 10 MeV. Additionally, comparison of the result with other investigations is provided.

Online non-contact surface finish measurement in machining using graph theory-based image analysis

This work addresses the following open research question: what non-contact measurement techniques and analytical approaches are required to assess the surface finish of a workpiece in situ during conventional machining without stopping the machine tool? The goal is to track variations in surface finish during conventional machining without stopping the machine tool so that quick compensatory action can be taken in case of a process drift. In pursuit of this goal, the objective of this work is non-contact, vision-based online measurement of surface finish in a conventional machining operation, such as outside diameter (OD) turning on a lathe. To realize the foregoing objective, algebraic graph theoretic image processing is used. The approach is based on converting an image of a surface into an unweighted and undirected network graph. The graph theoretic invariant, Fiedler number (λ2), is estimated, and subsequently invoked as a discriminant of workpiece surface roughness. The advantage of the proposed approach is that it eschews complex image filtering and segmentation steps. The central hypothesis is that the graph-based topological quantifier Fiedler number (λ2) estimates surface finish in a conventional machining operation with accuracy Sa±2μm (arithmetic average areal surface roughness, μm) when Sa is in the range of 1-10 μm. This hypothesis is tested on conventional cylindrical (outside diameter) turning of shafts by using an optical imaging setup (CCD camera) incorporated into a lathe machine. Through statistical modeling it is demonstrated that the Fiedler number (λ2) tracks surface finish variations in situ for steel and aluminum alloy shafts (4340 and 6061 grades, respectively) in near real-time with a maximum error of approximately Sa±2μm. This measurement error was verified to be within 15% of the actual measured surface finish. Tests were also carried out under three different rotational speeds (0rpm, 45rpm, 245rpm); and the approach was consequently attested to be robust to rotational speeds. The computational time for estimating the surface finish from this approach was assessed to be within tenth of a second, thus validating its practical applicability.