Thresholding-based Segmentation Technique Research Articles

Computed Tomography Assessment of Coronary Atherosclerosis: From Threshold-Based Evaluation to Histologically Validated Plaque Quantification.

Arterial plaque rupture and thrombosis is the primary cause of major cardiovascular and neurovascular events. The identification of atherosclerosis, especially high-risk plaques, is therefore crucial to identify high-risk patients and to implement preventive therapies. Computed tomography angiography has the ability to visualize and characterize vascular plaques. The standard methods for plaque evaluation rely on the assessment of plaque burden, stenosis severity, the presence of positive remodeling, napkin ring sign, and spotty calcification, as well as Hounsfield Unit (HU)-based thresholding for plaque quantification; the latter with multiple shortcomings. Semiautomated threshold-based segmentation techniques with predefined HU ranges identify and quantify limited plaque characteristics, such as low attenuation, non-calcified, and calcified plaque components. Contrary to HU-based thresholds, histologically validated plaque characterization, and quantification, an emerging Artificial intelligence-based approach has the ability to differentiate specific tissue types based on a biological correlate, such as lipid-rich necrotic core and intraplaque hemorrhage that determine plaque vulnerability. In this article, we review the relevance of plaque characterization and quantification and discuss the benefits and limitations of the currently available plaque assessment and classification techniques.

A learning-based approach to breast cancer screening using mammography images

<p>The current big challenge facing radiologists in healthcare is the automatic detection and classification of masses in breast mammogram images. In the last few years, many researchers have proposed various solutions to this problem. These solutions are effectively dependent and work on annotated breast image data. But these solutions fail when applied to unlabeled and non-annotated breast image data. Therefore, this paper provides the solution to this problem with the help of a neural network that considers any kind of unlabeled data for its procedure. In this solution, the algorithm automatically extracts tumors in images using a segmentation approach, and after that, the features of the tumor are extracted for further processing. This approach used a double thresholding-based segmentation technique to obtain a perfect location of the tumor region, which was not possible in existing techniques in the literature. The experimental results also show that the proposed algorithm provides better accuracy compared to the accuracy of existing algorithms in the literature.</p>

OESV-KRF: Optimal ensemble support vector kernel random forest based early detection and classification of skin diseases

The most widespread disease that often puts the patient’s life in danger is skin disease. Avoiding this situation, it necessitates the development of efficient and reliable skin cancer prediction techniques. Besides, regular screening of suspicious lesions in the skin helps the dermatologist to detect skin cancer in the early phase. Although there exist numerous Machine Learning methods to detect skin cancer in its early stage, the accurate and timely prediction and classification of skin lesions continued to be demanding. To fulfill this aim, in this paper, we proposed an ensemble support vector kernel random forest-based hybrid equilibrium Aquila optimization (ESVMKRF-HEAO) approach. The proposed prediction model is examined using the HAM10000 dataset which contains numerous varieties of skin lesion images in it. Initially, the intrusions and noises in the dataset are removed, and image qualities are enhanced using preprocessing pipelines. Then, the thresholding-based segmentation technique is utilized to segment the cancerous lesion regions from the healthy background. Finally, the proposed classifier accurately predicts and classifies the segmented images based on their feature characteristics into five distinct categories namely melanocytic nevus, basal cell carcinoma, melanoma, actinic keratosis and dermatofibroma. The simulation of the proposed model is performed using MATLAB 2019a software. The evaluation of the proposed ESVMKRF-HEAO approach performance is done concerning metrics such as sensitivity, f-1 score, accuracy, precision and specificity. The proposed ESVMKRF-HEAO approach attained a greater performance rate with respect to all metrics especially, the prediction accuracy of about 97.4% in the experimental results.

Optimal Artificial Intelligence Based Automated Skin Lesion Detection and Classification Model

Skin lesions have become a critical illness worldwide, and the earlier identification of skin lesions using dermoscopic images can raise the survival rate. Classification of the skin lesion from those dermoscopic images will be a tedious task. The accuracy of the classification of skin lesions is improved by the use of deep learning models. Recently, convolutional neural networks (CNN) have been established in this domain, and their techniques are extremely established for feature extraction, leading to enhanced classification. With this motivation, this study focuses on the design of artificial intelligence (AI) based solutions, particularly deep learning (DL) algorithms, to distinguish malignant skin lesions from benign lesions in dermoscopic images. This study presents an automated skin lesion detection and classification technique utilizing optimized stacked sparse autoencoder (OSSAE) based feature extractor with backpropagation neural network (BPNN), named the OSSAE-BPNN technique. The proposed technique contains a multi-level thresholding based segmentation technique for detecting the affected lesion region. In addition, the OSSAE based feature extractor and BPNN based classifier are employed for skin lesion diagnosis. Moreover, the parameter tuning of the SSAE model is carried out by the use of sea gull optimization (SGO) algorithm. To showcase the enhanced outcomes of the OSSAE-BPNN model, a comprehensive experimental analysis is performed on the benchmark dataset. The experimental findings demonstrated that the OSSAE-BPNN approach outperformed other current strategies in terms of several assessment metrics.

Automated hemodynamic assessment for cranial 4D flow MRI

Cranial 4D flow MRI post-processing typically involves manual user interaction which is time-consuming and associated with poor repeatability. The primary goal of this study is to develop a robust quantitative velocity tool (QVT) that utilizes threshold-based segmentation techniques to improve segmentation quality over prior approaches based on centerline processing schemes (CPS) that utilize k-means clustering segmentation. This tool also includes an interactive 3D display designed for simplified vessel selection and automated hemodynamic visualization and quantification.The performances of QVT and CPS were compared in vitro in a flow phantom and in vivo in 10 healthy participants. Vessel segmentations were compared with ground-truth computed tomography in vitro (29 locations) and manual segmentation in vivo (13 locations) using linear regression. Additionally, QVT and CPS MRI flow rates were compared to perivascular ultrasound flow in vitro using linear regression. To assess internal consistency of flow measures in vivo, conservation of flow was assessed at vessel junctions using linear regression and consistency of flow along vessel segments was analyzed by fitting a Gaussian distribution to a histogram of normalized flow values. Post-processing times were compared between the QVT and CPS using paired t-tests.Vessel areas segmented in vitro (CPS: slope = 0.71, r = 0.95 and QVT: slope = 1.03, r = 0.95) and in vivo (CPS: slope = 0.61, r = 0.96 and QVT: slope = 0.93, r = 0.96) were strongly correlated with ground-truth area measurements. However, CPS (using k-means segmentation) consistently underestimated vessel areas. Strong correlations were observed between QVT and ultrasound flow (slope = 0.98, r = 0.96) as well as flow at junctions (slope = 1.05, r = 0.98). Mean and standard deviation of flow along vessel segments was 9.33e-16 ± 3.05%. Additionally, the QVT demonstrated excellent interobserver agreement and significantly reduced post-processing by nearly 10 min (p < 0.001). By completely automating post-processing and providing an easy-to-use 3D visualization interface for interactive vessel selection and hemodynamic quantification, the QVT offers an efficient, robust, and repeatable means to analyze cranial 4D flow MRI. This software is freely available at: https://github.com/uwmri/QVT.

MudrockNet: Semantic segmentation of mudrock SEM images through deep learning

Segmentation and analysis of individual pores and grains of mudrocks from scanning electron microscope images is non-trivial because of imaging artifacts, variation in pixel grayscale values across images, and overlaps in grayscale values among different physical features such as silt grains, clay grains and pores, which make identifications difficult. Moreover, because grains and pores often have overlapping grayscale values, direct application of threshold-based segmentation techniques is not sufficient. Recent advances in the field of computer vision have made it easier and faster to segment images and identify multiple occurrences of such features in an image, provided that ground-truth data for training the algorithm are available. Here we propose a deep learning SEM image segmentation model, MudrockNet based on Google's DeepLab-v3+ architecture implemented with the TensorFlow library. The ground-truth data were obtained from an image-processing workflow applied to scanning electron microscope images of uncemented muds from the Kumano Basin offshore Japan at depths <1.1 km. The trained deep learning model obtained a pixel-accuracy > 90%, and predictions for the test data obtained a mean intersection over union (IoU) of 0.6663 for silt grains, 0.7797 for clay grains and 0.6751 for pores. We also compared our model with the random forest classifier using trainable Weka segmentation in ImageJ, and it was observed that MudrockNet gave better predictions for silt grains, clay grains and pores in most cases. The size, concentration, and spatial arrangement of the silt and clay grains can affect the petrophysical properties of a mudrock, and an automated method to accurately identify the different grains and pores in mudrocks can help improve reservoir and seal characterization for petroleum exploration and anthropogenic waste sequestration.

Efficient Segmentation of Lymphoblast in Acute Lymphocytic Leukemia

Microscopic examination of peripheral blood smears and bone marrow is the preliminary step for the diagnosis of several life-threatening diseases. Acute lymphocytic leukemia (ALL) is the most common disease in children that also needs an early diagnosis for on-time treatment as it spreads rapidly in the blood and forms immature lymphocytes. This might cause death in some weeks if left untreated. Manual methods in clinical laboratory being applied for the diagnosis of these diseases are inefficient and expensive, and the results are less accurate. A computer-aided system is the need of the day in which the most important step is segmenting the region of interest in blood or bone marrow for the detection and cure of the diseases which is the most challenging task. This study aims to propose a simple threshold-based segmentation technique by processing the S component of the HSV color space to segment the lymphoblasts in the bone marrow images of ALL patients. The technique was applied to 230 RGB bone marrow images having all the three types of ALL, i.e., L1, L2, and L3 resulted in the overall accuracy of 96.8%.

Performance Evaluation Towards Automatic Building and Road Detection Technique for High-Resolution Remote Sensing Images

Building and road detection from the high-resolution remote sensing images has many applications in a wide range of areas including urban design, real-estate management, and disaster relief. Extracting buildings and roads from the remote sensing images have been performed by human experts manually, but it takes too much time and the cost of labor to build is also very high. So, an automated system that can emulate a human operator is desired. Our goal is to develop a system for automatically detecting buildings and roads directly from high-resolution satellite images. Therefore, we propose Internal Gray Variance (IGV) to detect the buildings and roads in the urban areas. First, the satellite images are enhanced by using morphological operators, which enhance the edges of the objects. Then multiseed-based clustering technique detects the building and road edges using the variance in the gray levels. To reduce the false alarm, tiny regions that are improbable to be buildings and roads are separated. Finally, by using the adaptive threshold-based segmentation technique, the buildings are segmented and the road network is extracted based on supervised directional homogeneity. Finally, we evaluate our system on a large-scale road and building detection data sets that are publicly available.

Bi-Level algorithm for the segmentation and counting of Leukocytes and Erythrocytes

Background/Objectives: To present an accurate quantitative approach based two-phase algorithm to count both the leukocytes and erythrocytes for identifying the severity of leukaemia in the human body. Methods/Statistical analysis: The algorithm is having two-phases with the first phase meant for recognizing and counting the leukocytes using the thresholding based segmentation technique that focuses on the intensity values of pixels of the greyscale blood smear images; whereas the second phase recognizes the erythrocytes by their circular shape using Circular Hough Transform (CHT) method. The system experiments with 26 stained blood smear images from the ALL-IDB1 benchmark dataset. Findings: The first phase of the algorithm achieves 99.41 per cent overall accuracy in leukocytes detection and in the second phase 99.76 per cent overall accuracy is attained in erythrocytes detection. Novelty/Applications: This proposal applies Circular Hough Transform in detecting the erythrocytes by adjusting the radius of the circle according to the magnification rate of the sample image. Keywords: Circular Hough transform; cell count; image processing; Leukaemia; Leukocytes; Erythrocytes

Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images

Digital image processing has a significant role in different research areas, including medical image processing, object detection, biometrics, information hiding, and image compression. Image segmentation, which is one of the most important steps in processing medical image, makes the objects inside images more meaningful. For example, from microscopic images, blood cancer can be identified which is known as leukemia; for this purpose at first, the white blood cells (WBCs) need to be segmented. This paper focuses on developing a segmentation technique for segmenting WBCs from microscopic blood images based on thresholding segmentation technique and it compares with the most commonly used segmentation technique which is known as color-k-means clustering. The comparison is done based on three well-known measurements, used for evaluating segmentation techniques which are probability random index, variance of information, and global consistency error. Experimental results demonstrate that the proposed thresholding-based segmentation technique provides better results compared to color-k-means clustering technique for segmenting WBCs as well as the time consumption of the proposed technique is less than the color-k-means which are 70.8144 ms and 204.7188 ms, respectively.

An Automated Brain Tumour Boundary Detection using Region based Segmentation Technique along with SVM Classifier

We presents a fully automated method for an automated brain-tumour boundary detection using region based segmentation technique along with SVM Classifier of Magnetic Resonance Imaging (MRI).The procedure is based on artificial intelligence technique and classification of each super-pixel in MRI. A number of novel image features extraction approaches including intensity-based, texture based, fractal analysis and curvatures are calculated from each super-pixel within the entire brain area in MRI to ensure a robust classification. Brain tumor is the malignant types of cancer and their classification in earlier stage is biggest issue. While curable with early classification is useful, only extremely trained specialists are capable of accurately recognizing the cancer from skin MRI data. As expertise is in limited contribute, an automated systems capable of classifying cancer could save human lives, and also help to reduce unnecessary MRI, and reduce extra costs. On the way to achieve this goal, we proposed a Brain Tumour Detection and Classification System (BTDCS) that combines recent developments in machine learning with Support Vector Machine (SVM) structure, creating hybrid algorithm of threshold based segmentation with Maximally Stable External Regions (MSER) that are capable of segmenting accurate super-pixel region from MRI, as well as analyzing the detected area and surrounding tissue for malignant. Using threshold based segmentation technique, the foreground and background component is separated into two regions. To improve the segmentation results, MSER is used with the novel concept of region detection and feature extraction mechanism. The proposed system is evaluated using the largest publicly accessible standard BRATS 2015 dataset of MRI, containing benign and malignant images. When the evaluation parameters of proposed work is compared with a few other state-of-art methods, the proposed means attains the best performance of 98.2% concerning classification accuracy using only the MSER approach and SVM as classifier. The ultimate aim of this research is to devise an automated experimental approach that can segment the tumor boundary in a fast and efficient manner.

An intensity factorized thresholding based segmentation technique with gradient discrete wavelet fusion for diagnosing stroke and tumor in brain MRI

The detection of a brain tumor and stroke from the magnetic resonance imaging (MRI) is one of the critical tasks in recent days for neuro-radiologists. So, various segmentation techniques are developed customarily, but it fails to provide an accurate diagnosis. To elucidate this problem, this paper aims to develop a fusion based segmentation technique for the detection of MRI brain tumor and stroke. The MRI brain images considered here include T1-weighted (T1-w), T2-weighted (T2-w), Diffusion Weighted Imaging (DWI), and Fluid-attenuated Inversion Recovery (FLAIR). The first step in the proposed methodology includes Gradient based Discrete Wavelet Transform as an image fusion technique with the target gradient estimation process. The different image fusion combinations include T1-w and T2-w, T1-w and DWI, T1-w and FLAIR, T2-w and DWI, T2-w and FLAIR, DWI and FLAIR. Secondly, the visual quality of the image is improved by applying the histogram equalization method. Finally, an Intensity Factorized Thresholding technique is proposed for segmentation in order to emphasis the diagnosis of tumor and stroke affected region in the given MRI brain image based on the pixel intensity. Here, the segmented results of both original (non-fused) and fused images are evaluated for predicting the accurate region of tumor and stroke. During experiments, the performance of both existing and proposed techniques are evaluated by using various measures like sensitivity, specificity, accuracy, Positive Predictive Value, Negative Predictive Value, Rand Index, Global Consistency Error, Variation of Information, Jaccard and Dice coefficients. From the obtained result, it is concluded that fusion based segmentation technique is giving better results than non-fusion based segmentation techniques. Among the fusion based segmented result, T2-w and FLAIR fused segmented result is superior to other fusion combinations for detecting tumor. Similarly, DWI and FLAIR fused segmented result is better than other fusion combinations for diagnosing stroke.

Adaptive hysteresis thresholding segmentation technique for localizing the breast masses in the curve stitching domain

Background and objectiveMassive work by distinguished researchers in the domain of breast segmentation has been proposed. However, no significant solution reduces the limitations of the false positive rate of cancerous cells in the breast body for probing the abnormalities of particular features. This problem is challenging in its nature and essential to be solved. It is needed to reach the optimal measurements of the breast parenchyma, the breast patchy regions of the mammogram, or the breast registration for searching of precise oddities. MethodsIn this work, we propose a novel approach for observing the abnormal breast cells identification with high sensitivity. A cancer tumor often produces a specific protein in the blood that serves as a marker for the cancer cells. These cells break off from the cancer and move into the blood stream. However, presence of pectoral muscle in breast mammogram highly affects the detection process of breast tumor. A novel aspect of the proposed method is that the curve stitching technique is developed for removing of pectoral muscle. Following this, an adaptive hysteresis thresholding is used for segmentation. This hybrid technique is used for segmenting a breast region of digital mammogram with suppression of pectoral muscle. ResultsThe proposed method attains a highest sensitivity rate of 96.6% for the MIAS dataset and 96.4% for the DDSM dataset as compared to existing methods. ConclusionThe main idea behind this is to improve the threshold based segmentation techniques to create an adaptive threshold and apposite templates, in order to conserve tumor salient features about suspicious regions to classify benign and malignant mass in mammogram. First, a spline based curve fitting is applied on edges of the breast parenchyma and fill the region with a very low intensity value and map on original image to preserve the original intensity of breast region free of pectoral muscle. The results of the experiments show that the proposed segmentation technique is efficient when tested on MIAS and DDSM dataset.

Statistical analysis of optical coherent tomography image by segmentation methods using euclidean distant model

Optical Coherence Tomography (OCT) is a diagnostic measure of the images in retinal layers. The measure of retinal layers is done by image segmentation algorithm. In this work, algorithm of different threshold based segmentation techniques is computed in OCT image. Using the resultant image, comparison is done between the segmentation methods by statistical parameters such as noise factor of the signal peak, Normalized Absolute Error (NAE) and Average Difference (AD). Statistical analysis values are evaluated by multidimensional scaling (MDS) methods. By applying clustering methods in the multidimensional data, the Euclidean Distance model gives the relation distant between the algorithms. By this result, similarity or dissimilarity of segmentation methods was categorized.

A New Breast Border Extraction and Contrast Enhancement Technique with Digital Mammogram Images for Improved Detection of Breast Cancer

Purpose:Breast cancer can be cured if diagnosed early, with digital mammography which is one of the most effective imaging modalities for early detection. However mammogram images often come with low contrast, high background noises and artifacts, making diagnosis difficult. The purpose of this research is to preprocess mammogram images to improve results with a computer aided diagnosis system. The focus is on three preprocessing methods: a breast border segmentation method; a contrast enhancement method; and a pectoral muscle removal method.Methods:The proposed breast border extraction method employs a threshold based segmentation technique along with a combination of morphological operations. The contrast enhancement method presented here is divided into two phages. In phase I, a bi-level histogram modification technique is applied to enhance the image globally and in phase II a non-linear filter based on local mean and local standard deviation for each pixel is applied to the histogram modified image. The pectoral muscle removal method discussed here is implemented by applying a region growing algorithm.Results:The proposed techniques are tested with the Mini MIAS dataset. The breast border extraction method is applied to 322 images and achieved 98.7% segmentation accuracy. The contrast enhancement method is evaluated based on quantitative measures like measure of enhancement, absolute mean brightness error, combined enhancement measure and discrete entropy. The proposed contrast enhancement method when applied to 14 images with different types of masses, the quantitative measures showed an optimum level of contrast enhancement compared to other enhancement methods with preservation of local detail. Removal of the pectoral muscle from MLO mammogram images reduced the search region while identifying abnormalities like masses and calcification.Conclusions:The preprocessing steps proposed here show promising results in terms of both qualitative and quantitative analysis.

Object-Based Metrics for Forecast Verification of Convective Development with Geostationary Satellite Data

AbstractObject-based metrics are adapted and applied to geostationary satellite observations with the evaluation of cloud forecasts in convective situations as the goal. Forecasts of the convection-permitting German-focused Consortium for Small-Scale Modeling (COSMO-DE) numerical model are transformed into synthetic observations using the RTTOV radiative transfer model, and contrasted with the corresponding real observations. Threshold-based segmentation techniques are applied to the fields for object identification. The statistical properties of the traditional measures cold cloud cover and average brightness temperature amplitude are contrasted to object-based metrics of spatial aggregation and object structure. Based on 59 case days from the summer half-years between 2012 and 2014, a variance decomposition technique is applied to the time series of the metrics to identify deficits in day-to-day, diurnal, and weather-regime-related variability of cold cloud characteristics in the forecasts. Furthermore, sensitivities of the considered metrics are discussed, which result from uncertainties in the satellite forward operator and from the choice of parameters in the object identification techniques.

P53immunostained cell nuclei segmentation in tissue images of oral squamous cell carcinoma

This paper presents segmentation of p53immunostained tissue images of oral squamous cell carcinoma that consist of cell nuclei segmentation and splitting of overlapping/touching cell structures. In segmentation, the entropy thresholding has been adopted in which the optimum threshold value to each color component of the image is obtained by maximizing the global entropy computed from its gray-level co-occurrence matrix. The segmented image consists of isolated cells and complex nuclei structures. A novel complex nuclei structure detection algorithm is proposed to identify overlapped nuclei structures, which have been further resolved by watershed transform. The performance of the segmentation technique is evaluated using the quantitative metrics, namely mean absolute difference (MAD), dice coefficient (DC) and accuracy. Global entropy thresholding-based segmentation technique achieved the best MAD of 0.478, DC of 0.967 and accuracy of 0.970 compared to state-of-art techniques such as otsu and active contour. Extensive experimental results show that proposed complex nuclei structure detection-based overlapping/touching cells splitting algorithm effectively delineated nuclei with over- and under-segmentation rate of 0.49 %. Therefore, tissue image segmentation method presented has high potential in immunohistochemical (IHC) quantification and also can be easily generalized for images stained with other biomarkers.

Threshold Based Segmentation Technique for Mass Detection in Mammography

Threshold Based Segmentation Technique for Mass Detection in Mammography

Automatic Building Detection From High-Resolution Satellite Images Based on Morphology and Internal Gray Variance

Automatic building extraction remains an open research topic in digital photogrammetry and remote sensing. While many algorithms have been proposed for building extraction, none of them solve the problem completely. This is even a greater challenge in urban areas, due to high-object density and scene complexity. Standard approaches do not achieve satisfactory performance, especially with high-resolution satellite images. This paper presents a novel framework for reliable and accurate building extraction from high-resolution panchromatic images. Proposed framework exploits the domain knowledge (spatial and spectral properties) about the nature of objects in the scene, their optical interactions and their impact on the resulting image. The steps in the approach consist of 1) directional morphological enhancement; 2) multiseed-based clustering technique using internal gray variance (IGV); 3) shadow detection; 4) false alarm reduction using positional information of both building edge and shadow; and 5) adaptive threshold based segmentation technique. We have evaluated the algorithm using a variety of images from IKONOS and QuickBird satellites. The results demonstrate that the proposed algorithm is both accurate and efficient.

An Efficient Indexing Approach for Content based Image Retrieval

this paper an efficient indexing and retrieval technique is proposed for identification of plant images. The plant images have been retrieved as herbs shrubs and trees based on color and texture features. Plant images have rich content of information such as leaves, bark and stem etc. This content can be extracted as various content features. Plant image database often represent the image objects as high- dimensional feature vectors and access them via the feature vectors and similarity measures. Hence in order to retrieve plant images an indexing method is proposed based image retrieval to improve the retrieval performance. In this work color and texture features are extracted for characterizing leafy mass and stem images of plants. There are 20 images of five plant species, amounting to total of 100 images, herbs, shrubs, and trees. Hence, totally there are 300 images of leafy mass and bark of 15 plant species. All the images are captured in natural environment using a digital camera. In the pre- processing stage, the images of leafy mass and bark with complex background information and noise is filtered out by using Unsharp filtering method. In the segmentation method 2D-OTSU threshold based segmentation technique is used to separate the object from the background For Ex: small leaves surrounded by bright sky, small openings among dense canopy, and mixed pixels. For feature extraction the Modified Color Co-occurrence Matrix (MCCM) and Gabour Filter is used to retrieve the color and texture of the images from the database. Usually the retrieval performance of an image retrieval system is greatly influenced by different similarities or distance measures. We have used Euclidean distance measures for similarity matching. Finally for retrieval of the images from the database Fast Indexing scheme for CBIR method is used. Then multi-dimensional indexing technique is employed using tree structures, R* tree. The effective indexing and fast searching of images on bases of visual features pose a significant issue in CBIR. We have used R*- Tree structure to achieve better performance and efficiency. A graph is plotted to compare the retrieval result of both technique. Indexing Technique gives better retrieval rate.