MATLAB Image Processing Toolbox Research Articles

Development and Evaluation of a Sensor Enabled Smart Herbicide Applicator for Inter Row Crops

A novel self-propelled smart herbicide applicator (1600mm × 500mm × 1100mm) was developed using machine vision to target weeds and apply herbicides variably in wide row-spaced crops. The applicator integrated a weed detection system, smart sprayer, mechanical components, and power transmission system. The weed detection system used a webcam based image sensor, with images processed via MATLAB's image processing toolbox. The system processed images using an RGB-based algorithm (R > G + 5, R > B + 5) to separate weeds from soil. Weed density, calculated as the green area percentage (green pixels to total pixels), was sent to a microcontroller-operated smart sprayer via a serial port. The microcontroller regulates herbicide flow by controlling the valve with a servo motor, following an algorithm based on green area percentage. Field tests showed a 40% reduction in herbicide use, 91.26% weeding efficiency, and 63.4% field efficiency. The smart herbicide applicator's unique features include real-time measurement, portability, and precise herbicide application for wide-row crops, even at night. It also reduced environmental hazards and drudgery among farmers.

89 Streamlining the acquisition and analysis of digital images for the assessment of horn fly abundance

Abstract Horn flies are one of the most prevalent ectoparasites on pasture raised cattle. Studies have shown reasonable evidence of a genetic basis for horn fly attraction in beef cattle. Current methods for assessing fly abundance-related phenotypes (e.g., subjective assessment and image-based counts) suffer from limited accuracy and logistic difficulties. Previous attempts to assess horn fly abundance using image-based methods were hindered by several factors including weather conditions, distance and angle between the image taker and the animal, and lighting during image acquisition. The aim of this study was to address these limitations through the development of practical guidance to optimize image acquisition and to develop an adaptive image analysis algorithm to enhance the accuracy of image-based assessment of fly abundance. Images of beef cattle on pasture over multiple horn fly seasons (late spring to early fall) were collected. For each animal, the best image (where the entire side of the animal was in view) was selected and cropped from the withers to the hooks and from the chest floor to the udder. For every image (n = 314), all the flies were manually counted using the ImageJ software. Several image enhancement and analysis techniques including adaptive thresholding, pixel region connection, and pixel classification were used. The images were then clustered into three quality classes based on quality metrics computed using several algorithms from the image processing toolbox in MATLAB (BRISQUE, NIQE, and PIQE). BRISQUE and NIQE use distortions of natural scene statistics to compute quality scores. PIQE uses image features and models of human perception to compute quality scores. Pearson correlation, mean squared error (MSE), and the coefficient of determination (R2) were used to evaluate the concordance between the manual (ground truth) and the image predicted counts. Across the different scenarios, the Pearson correlation and R2 ranged between 0.21 to 0.67 and 0.04 and 0.45, respectively (Table 1). When the images were first clustered in quality classes, all the assessment metrics showed a substantial increase (Table 2). In fact, the Pearson correlation ranged between 0.47 to 0.80 using the NIQE. When the images were classified as low or medium quality by BRISQUE, the correlation was greater (0.74 to 0.79) compared with NIQE (0.47 to 0.70) and PIQE (0.69 to 0.74). Similar patterns were observed for the other assessment criteria (MSE and R2). These results seem to indicate that NIQE, BRISQUE, or a combination of the two algorithms could be used to assess image quality and determine the appropriate combination of image parameters to maximize the predictive ability. The NIQE method could be used as a filter to detect low quality images that are unsuitable for counting and then BRISQUE could be used on the remaining images.

Experimental investigation of electrode wear assessment in the EDM process using image processing technique

Abstract One of the widespread non-traditional and non-contact machining processes is the electrical discharge machining (EDM) process, which depends on removing material from the surface by different repeat electrical discharges between a machined workpiece and a cutting tool called the electrode. The electrode wear in the EDM process is one of the parameters of significant technological interest. This article proposed a reliable image-based system that automatically detects and computes electrode wear in EDM of rectangular steel workpiece plate of AISI 314 using a copper electrode with a rectangular shape. MATLAB environment and image processing toolbox are used to identify and manipulate the captured rectangular electrode image for rectangular electrode data extraction needed for the electrode wear identification process. The results show that the proposed image-based approach, using a non-contact measuring system and relatively inexpensive equipment, is suitable with good accuracy and efficiency for measuring and testing electrode wear.

Potential Disease Severity Assessment Approach for Potato Late Blight with Smartphone-Based Image Capturing

Accurate and timely evaluation of the severity of Potato Late Blight (PLB) is vital in disease control and being confident that fungicides are utilized efficiently. PLB is caused by Phytophthora infestans and is one of the most destructive diseases affecting potatoes. Knowledge gap in determining the severity of the PLB using the Late Blight Key (LBK) results in erroneous disease evaluation, unregulated chemical use, and exorbitant management expenses for PLB. The purpose of this research was to build a novel technique based on smartphones to measure the PLB severity in relation to the LBK. Using a smartphone camera, digital images of sick PLB leaves were taken at a consistent height, and simultaneously the related visual disease severity rating score was recorded using the LBK. The MATLAB (R2019b) image processing toolbox was used to process the photos, which included cropping, manually defining the areas of the leaf and lesion, and determining the pixel ratio (the ratio of PLB lesion to leaf pixels). When data from all the images were merged at the conclusion, regression analysis revealed a virtually perfect linear association (R2=86.6%) between the image-derived pixel ratio and the visual ranking scores of the PLB sickness severity. According to these results, image processing analysis that has been built may precisely identify the leaf areas that are infected with PLB and assess the disease's severity level.

Experimental Investigation of Surface Roughness and Material Removal Rate in Wire EDM of Stainless Steel 304.

Its unexcelled mechanical and physical properties, in addition to its biocompatibility, have made stainless steel 304 a prime candidate for a wide range of applications. Among different manufacturing techniques, electrical discharge machining (EDM) has shown high potential in processing stainless steel 304 in a controllable manner. This paper reports the results of an experimental investigation into the effect of the process parameters on the obtainable surface roughness and material removal rate of stainless steel 304, when slotted using wire EDM. A full factorial design of the experiment was followed when conducting experimental trials in which the effects of the different levels of the five process parameters; applied voltage, traverse feed, pulse-on time, pulse-off time, and current intensity were investigated. The geometry of the cut slots was characterized using the MATLAB image processing toolbox to detect the edge and precise width of the cut slot along its entire length to determine the material removal rate. In addition, the surface roughness of the side walls of the slots were characterized, and the roughness average was evaluated for the range of the process parameters being examined. The effect of the five process parameters on both responses were studied, and the results revealed that the material removal rate is significantly influenced by feed (p-value = 9.72 × 10-29), followed by current tension (p-value = 6.02 × 10-7), and voltage (p-value = 3.77 × 10-5), while the most significant parameters affecting the surface roughness are current tension (p-value = 1.89 × 10-7), followed by pulse-on time (1.602 × 10-5), and pulse-off time (0.0204). The developed regression models and associated prediction plots offer a reliable tool to predict the effect of the process parameters, and thus enable the optimizing of their effects on both responses; surface roughness and material removal rate. The results also reveal the trade-off between the effect of significant process parameters on the material removal rate and surface roughness. This points out the need for a robust multi-objective optimization technique to identify the process window for obtaining high quality surfaces while keeping the material removal rate as high as possible.

Investigating Visual Monitoring of the Scrotum as a Supplementary Tool for Boar Semen Quality Evaluation.

Farm animals behavior research uses video cameras, mainly for visual observation and recording. The purpose of this feasibility study was to enrich the predictable methods of boar semen production capacity by correlating sperm variables with the scrotal contractions (SC) frequency and intensity. A video camera was used to record the reaction of the scrotum during ejaculation. The respective collected ejaculates were evaluated and semen parameters, such as viability, morphology, membranes functional integrity and kinematics, were determined. The camera recorded the scrotal contractions/relaxations and the video was handled by the Image Processing Toolbox of Matlab (Mathworks Inc., Natick, MA, USA). The SC intensity was verified as a percentage change in the scrotum size among the video frames of maximum contraction and relaxation. The archived data from the frames were analyzed statistically, using a linear mixed effects model that involved sperm assessed parameters. Correlations of the SC intensity with the average path velocity, VAP (R2 = 0.591, p = 0.043) and with the percentage of the cytoplasmic droplets (R2 = 0.509, p = 0.036) were noticed. Previous studies reported the positive correlation of VAP with the number of live-born piglets. In conclusion, video monitoring of the boar scrotal function during ejaculation is useful, but more research is needed to establish its appropriateness as a supplementary method for the prognosis of boar ability to produce high-quality semen.

Spatial distribution patterns of trunk internal decay of Euphrates poplar riparian forest along the Tarim River, northwest China

Trunk internal decay is a common phenomenon in forest ecosystems, causing the loss of forest resources. Euphrates poplar (Populus euphratica Oliv.) is the only constructive tree species of the desert riparian forests along the Tarim River in northwestern China. The ecosystem stability of these forests has been damaged due to widespread internal decay or hollow rot in many tree trunks. However, forest resource conservation is difficult due to the unclear spatial patterns of P. euphratica trunk decay under different water conditions and tree morphology in the middle (MTR) and lower reaches (LTR) of the Tarim River. We conducted a field experiment and lab analysis to assess the reliability of the ARBOTOM® 2D stress wave Non-Destructive Testing (NDT) technique on P. euphratica populations. We explored the quantitative characterization method and the spatial distribution patterns of P. euphratica trunk internal decay in the MTR and LTR. Our study found that (1) the accuracy of the stress wave NDT technique in detecting trunk decay of P. euphratica in the arid desert area was more than 80%; The severity of internal trunk decays can be quantitatively characterized by the combination of the Matlab Image Processing Toolbox, (2) the trunk decay proportion of P. euphratica showed an increasing trend with the tree height in the MTR. The LTR first increased, then decreased. The degree of decay was highest when the tree height was 6–7 m. The trunk decay proportion of individual trees positively correlated with the diameter at breast height and crown loss (p < 0.001) and negatively correlated with the crown diameter (p < 0.001). The higher the decay occurrence, the worse the individual size was, and vice versa, and (3) the decay proportion of the P. euphratica showed an overall increasing trend with an increase in groundwater depth. The trunk decay was significantly higher in the LTR compared to the MTR at the same distance of 300–1050 m from the river channel. Our findings suggested that the trunk decay in P. euphratica had a higher difference among different tree morphology, and the occurrence of decay in the LTR was much higher than in the MTR. The water stress at the site is one of the leading factors in internal defects in the riparian forests along the Tarim River. The results provide a theoretical basis and scientific support for desert riparian forest ecosystems’ conservation and sustainable management.

Surface Analysis in Additive Manufacturing Using Image Processing

Fused Deposition Modeling (FDM) is a type of additive manufacturing process where prototypes are developed by depositing layers on previously deposited layer of material. Surface roughness is an important factor in determining quality of produced parts. This work proposes a methodology to calculate the surface roughness of part manufactured using FDM process. The surface roughness values to be measured using conventional stylus instrument and Digital image processing technique. Stylus comes in direct contact with surface so it destroys the surface topography. Image processing is a non-contact method hence it overcomes this problem. A camera and directional lightings will be used to capture images of surface of specimens. These images will be analyzed and processed using various image processing techniques available in MATLAB image processing toolbox. Experimental results will be validated by comparing the results obtained by using image processing technique with conventional stylus system. The system is also proposed to inspect surface irregularities. Key Words: Surface roughness, image processing, MATLAB, FDM.

Assessing the Homogeneity of Forage Mixtures Using an RGB Camera as Exemplified by Cattle Rations

The article examines existing methods for assessing the homogeneity of feed mixes in the feed industry and agriculture. As an alternative to existing approaches, the authors offer a low-cost online technology to assess the homogeneity of feed. The feedstuff under study includes feed mixes for cattle or compound feed consisting of naturally-grown feed (green mass of freshly cut grass, haylage, corn silage, etc.) and concentrated components (grain milling, sunflower, and soybean meal). The proposed method based on an RGB camera, a diode lamp, filters, and software is approved by a preliminary study of the physical properties of feed mix components by Specim IQ hyperspectral camera and revealing characteristics of light absorption of each type of components (concentrated/natural origin). The article presents a method of processing a feed mix image fixed by an RGB camera through light filters using Matlab Image Processing Toolbox tools, namely, a Color Thresholder app and Image Region Analyzer app.

Computing Color Transforms with Applications to Image Editing

This paper deals with the implementation of various MATLAB functions present in image processing toolbox of MATLAB and using the same to create a basic image processor having different features like, viewing the red, green and blue components of a color image separately, color detection and various other features (noise addition and removal, edge detection, cropping, resizing, rotation, histogram adjust, brightness control, etc.) that is used in a basic image editor along with object detection and tracking.

The characteristics of alkali-activated slag mortar containing multi-walled carbon nanotubes at elevated temperature

The effect of the addition of different concentrations (0, 0.05, 0.10 and 0.15 wt%) of multi-walled carbon nanotubes (MWCNTs) on the mechanical properties and microstructure of alkali-activated slag (AAS) mortars at temperatures of 23, 200, 400, 600 and 800°C was investigated. To investigate the strength parameters of the specimens, the mechanical strength and mass loss of the specimens at ambient temperatures and after exposure to elevated temperatures were determined. Petrographic image analysis was also conducted to analyse the microstructures of the specimens. The Matlab image processing toolbox was used to calculate the pore area of the samples at room temperature. The results showed that the addition of MWCNTs improved the mechanical properties of the AAS mortars at both ambient and elevated temperatures: the compressive and flexural strengths of the mortars were improved by up to 69% and 85%, respectively. At 400°C, the compressive strength increase of the specimens compensated for the deterioration rate of about 3% mass loss. This temperature should be considered the maximum tolerable degree for AAS mortars.

MatGel: A MATLAB program for quantitative analysis of 2D polyacrylamide electrophoresis (2D-PAGE) protein gel images

Two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) is widely used in proteomics studies. Hundreds of proteins extracted from a biological sample can be separated and visualized on a 2D-PAGE gel. The interpretation of protein expression levels relies on the comparison of areas and intensities of the corresponding protein spots in 2D-PAGE gel images. However, determination of protein spot areas by the manual selection method is time-consuming and error-prone. The purpose of this research is to develop a highly automated program for the simultaneous detection and quantification of protein spots across a large number of 2D-PAGE protein gel images by using MATLAB image processing toolbox. This program will enhance the potential of using 2D-PAGE technique as a high throughput quantitative protein expression study tool. We developed MatGel, a simple and efficient program for protein spot area detection and intensity quantification from 2D-PAGE protein gel images. MatGel can detect and output the areas and mean intensities of corresponding protein spots across a large number of 2D-PAGE gel images simultaneously. Users also have options to adjust preferences at each step of image analysis. Basic knowledge with MATLAB programming language is required to run the program.•We developed MATLAB program MatGel to automate the determination of protein spots on 2D-PAGE protein gel images.•MatGel can analyze a large number of 2D-PAGE gel images simultaneously to minimize human errors.•MatGel is flexible and easy to use.

Fuzzy logic classification of mature tomatoes based on physical properties fusion

Grading of fruits and vegetables is an initial step after harvesting. It is also an essential operation before packaging. In the present study, different fuzzy algorithms for classification of mature tomato were applied and evaluated based on combinations of fruit color, size and hardness. Fuzzy membership functions of hardness were established by subjecting samples to Instron compression test as well as the rates of panelists. Each sample was also used for image processing to determine the color and size of fruit using Matlab image processing toolbox. Color and size fuzzy membership functions were established by published standard. The fuzzy If-Then rules were applied to classify the samples within five group outputs viz. “grade I”, “grade II”, “grade I-far market”, “processing”, and “storage”. Eighty-one fuzzy rules were reduced to 25 rules by combining the compatible rules. Six fuzzy algorithms with different fuzzifiers (zmf, sigmf, gbellmf) and defuzzifiers (bisector, mom, and centroid) were applied, and the outputs were compared to the panelists’ classifications in cross tables. According to the classification results, fuzzy algorithms grouped the fruits into correct classes with 90.9%, 92.3%, 88.7%, 87.4%, 92.4% and 93.3% accuracy for 6 models, respectively. The best result was observed with zmf and sigmf, and gbellmf as fuzzifier and mom as defuzzifier with 93.3% accuracy. Overly, the results revealed that the fusion of aforementioned tomato properties based on fuzzy membership functions could accurately classify the tomatoes in correct groups for different markets.

Improving the reconstruction of dental occlusion using a reconstructed-based identical matrix point technique

Digital dental models are widely used compared to dental impressions or plaster-dental models for occlusal analysis as well as fabrication of prosthodontic and orthodontic appliances. The digital dental model has been considered as one of the significant measures for the analysis of dental occlusion. However, the process requires more computation time with less accuracy during the re-establishment of dental occlusion. In this research, a modern method to re-establish dental occlusion has been designed using a Reconstructed-based Identical Matrix Point (RIMP) technique. The curvature of the dental regions has been reconstructed using distance mapping in order to minimize the computation time, and an iterative point matching approach is used for accurate re-establishment. Satisfactory restoration and occlusion tests have been analyzed using a dental experimental setup with high-quality digital camera images. Further, the high-quality camera images are converted to grayscale images for mathematical computation using MATLAB image processing toolbox. Besides, 70 images have been taken into consideration in which 30 planar view images has been utilized for experimental analysis. Indeed, based on the outcomes, the proposed RIMP outperforms overall accuracy of (91.50%) and efficiency of (87.50%) in comparison with conventional methods such as GLCM, PCR, Fuzzy C Means, OPOS, and OGS.

Automatic Attendance Recording System Using Facial Recognition

The student attendance is collected either manually or digitally through an RFID card. Recently, some of the institutions adopted the use of dynamic QR codes for attendance monitoring. However, these systems are challenging to maintain and time-consuming. The student can easily manipulate signatures for attendance monitoring. Moreover, the QR code system requires the application on both the lecturer and student sides. The lecturer needs to set up the QR code while using the mobile app to use the system. Therefore, it is time-consuming and sacrifices several minutes at the beginning of each class. The student also sometimes has difficulty in using the app leading to manual data insertion. Therefore, this project aims to solve this issue by using the facial recognition system on a lecturer’s laptop to collect the attendances. This project requires the use of a webcam and MATLAB to perform facial image recognition. This work utilizes face detection algorithm in the MATLAB image processing toolbox to build a system that will detect and recognize the frontal faces of students in a classroom. Firstly, several images of students will be recorded in a database. Then, a GUI-based application automatically identifies a face and matches it with the database created. Lecturers will use the GUI on their laptops, and students will show their faces to the web camera for their attendance to be taken. The system was tested successfully where student face was successfully recognized and recorded in the attendance monitoring system. Only registered user faces will be detected.

Development and Validation of an Online Analyzer for Particle Size Distribution in Conveyor Belts

Online measurement of particle size distribution in the crushing process is critical to reduce particle obstruction and to reduce energy consumption. Nevertheless, commercial systems to determine size distribution do not accurately identify large particles (20–250 mm), leading to particle obstruction, increasing energy consumption, and reducing equipment availability. To solve this problem, an online sensor prototype was designed, implemented, and validated in a copper ore plant. The sensor is based on 2D images and specific detection algorithms. The system consists of a camera (1024 p) mounted on the conveyor belt and image processing software, which improves the detection of large particle edges. The algorithms determine the geometry of each particle, from a sequence of digital photographs. For the development of the software, noise reduction algorithms were evaluated and selected, and a routine was designed to incorporate morphological mathematics (erosion, dilation, opening, lock) and segmentation algorithms (Roberts, Prewitt, Sobel, Laplacian–Gaussian, Canny, watershed, geodesic transform). The software was implemented (in MatLab Image Processing Toolbox) based on the 3D equivalent diameter (using major and minor axes, assuming an oblate spheroid). The size distribution adjusted to the Rosin Rammler function in the major axis. To test the sensor capabilities, laboratory images were used, where the results show a precision of 5% in Rosin Rambler model fitting. To validate the large particle detection algorithms, a pilot test was implemented in a large mining company in Chile. The accuracy of large particle detection was 60% to 67% depending on the crushing stage. In conclusion, it is shown that the prototype and software allow online measurement of large particle sizes, which provides useful information for screening equipment maintenance and control of crushers’ open size setting, reducing the obstruction risk and increasing operational availability.

Image Processing for the Identification of Persistent Stress from Retina

Chronic stress is a prolonged pressure that an individual faces over a long period. Chronic stress accounts for 80% of all health problems in the United States, and many other countries like the UK, Germany, India, etc. This can be challenging because it disturbs the immune system, eye vision, digestive system, sleep, and reproductive system. From the retinal images, few parameters are calculated for each disease. To detect glaucoma cup to disk ratio is calculated whereas for CSR area around the macula region is measured and to know whether a person is suffering from hypertension an AV ratio is calculated. The objective of this research is to identify whether a patient is under persistent depression. The retinal images of the patient are the inputs. They are then checked for three main diseases - glaucoma, central serous retinopathy, hypertension that are related to chronic stress. All the parameters are compared with a normal eye. If a person suffers from two or more of these diseases, he or she is evident to suffer from persistent stress. This entire experiment is simulated on MATLAB R2020a software. All the images are from Kaggle dataset and processed using image processing toolbox in MATLAB. The results are validated by ophthalmologist manually.

APPLICATION OF ZONAL AND CURVATURE FEATURES TO NUMERALS RECOGNITION

Purpose of the study: The purpose of this work is to present an offline Optical Character Recognition system to recognise handwritten English numerals to help automation of document reading. It helps to avoid tedious and time-consuming manual typing to key in important information in a computer system to preserve it for a longer time.&#x0D; Methodology: This work applies Curvature Features of English numeral images by encoding them in terms of distance and slope. The finer local details of images have been extracted by using Zonal features. The feature vectors obtained from the combination of these features have been fed to the KNN classifier. The whole work has been executed using the MatLab Image Processing toolbox.&#x0D; Main Findings: The system produces an average recognition rate of 96.67% with K=1 whereas, with K=3, the rate increased to 97% with corresponding errors of 3.33% and 3% respectively. Out of all the ten numerals, some numerals like ‘3’ and ‘8’ have shown respectively lower recognition rates. It is because of the similarity between their structures.&#x0D; Applications of this study: The proposed work is related to the recognition of English numerals. The model can be used widely for recognition of any pattern like signature verification, face recognition, character or word recognition in another language under Natural Language Processing, etc.&#x0D; Novelty/Originality of this study: The novelty of the work lies in the process of feature extraction. Curves present in the structure of a numeral sample have been encoded based on distance and slope thereby presenting Distance features and Slope features. Vertical Delta Distance Coding (VDDC) and Horizontal Delta Distance Coding (HDDC) encode a curve from vertical and horizontal directions to reveal concavity and convexity from different angles.

Quantification method of suspended solids in micromodel using image analysis

Micromodel can provide valuable information to improve understanding of pore-scale transport phenomenon and can also be utilized to simulate the transport process at pore scale. This research aims to propose settlement option for quantification of suspended solids in micromodel. The micromodel is used to mimic the formation damage which occurs in reservoir formation that could simultaneously affect enhanced oil recovery. This is done by utilizing visual image interpretation through image analysis on micromodel chip. Following the quantification of suspended solids, the micromodel was injected with brine that eventually forms agglomeration. Images are taken from NIS-Element AR microscope automatically in RGB color profile and then made into grayscale and finally into binary modes. Since the micromodel is simulated in 2D form structure, the quantification method complemented with image analysis is focusing on the quantified area, µm2 region of interest categorized into 3 main groups of area B05, M45 and T50, respectively. This research will explore on segmentation and thresholding processes of the visual data acquired from micromodel experiment. An image-based computational algorithm is programmed in MATLAB Image Processing Toolbox and ImageJ; hence, suspended solids in porous media could be quantified from the visual image executed in micromodel.

Enhance the LZW Compression Ratio Through the Use of Image Preprocessing Techniques for Gray Scale Images

Compression ratios of encoding algorithms degrade due to signal distortion, additive noise, and hacker manipulation. Large file size costs too much disk space, difficult to analyze, and high bandwidth to transmit over the internet. In this case, compression is mandatory. LZW is a general dictionary-based lossless compression algorithm. It is fast, simple, and efficient when it includes lots of repetitive data or monochrome images. Images with little data repetition and too much-blurred signal, the compression ratio of the LZW algorithm downgraded. Besides this, the execution time of the LZW compression algorithm increases dramatically. To preprocess and analyze the image information the researcher uses LZW encoding algorithm, bit plane slicing technique, Adaptive Median Filter, and MATLAB image processing toolbox. The MATLAB public grayscale image, salt &amp; pepper, Gaussian locavore blurred, and Bayern pattern image data sets are used. Those images dataset is used to test the normal LZW encoding algorithm and the proposed encoding algorithm compression ratio step by step. The noised dataset, the filtered datasets, and bit plane dataset images are processed and recorded quality and compression ratio parameters. The enhanced encoding algorithm average compression ratio is better by far from the normal LZW encoding algorithm by 160%. Not only has the compression ratio, but demising also improved the algorithms execution time. And the image quality metrics measurement of mean square error, peak signal to noise ratio, and structural similarity index measurement are 0, 99, and 1 respectively. This implies the enhanced encoding algorithm could decompress fully without scarifying image quality. The LZW encoding algorithm developmental environment specifies to select tiff and gif image formats. In addition, the LZW encoding algorithm functions are not available in the MATLAB image processing toolbox. The researcher challenged to write a MATLAB script for each personal function. Still, there is room to extend the compression ratio of the LZW encoding algorithm using the image masking technique.