Chain Code Research Articles

Boolean operations on rasterized shapes represented by chain codes using space filling curves

This paper introduces a new algorithm for Boolean operations on rasterized geometric shapes that are represented with chain codes. The algorithm works in three steps. Firstly, the chain code symbols are transformed in the Hilbert space, where the overlaid chain code symbols are recognised. After that, a suitable starting cell is determined. Finally, the walk-about through the sequence of the initial chain code symbols is performed to obtain the sequence of chain code symbols representing the shape of the required Boolean operation. The algorithm is demonstrated on Freeman chain code in four directions. The time and space complexity of the proposed algorithm is linear, which was proven theoretically and confirmed by experiments.

Sustained secretion of anti-tumor necrosis factor α monoclonal antibody from ex vivo genetically engineered dermal tissue demonstrates therapeutic activity in mouse model of rheumatoid arthritis.

Rheumatoid arthritis (RA) is a symmetric inflammatory polyarthritis associated with high concentrations of pro-inflammatory, cytokines including tumor necrosis factor (TNF)-α. Adalimumab is a monoclonal antibody (mAb) that binds TNF-α, and is widely used to treat RA. Despite its proven clinical efficacy, adalimumab and other therapeutic mAbs have disadvantages, including the requirement for repeated bolus injections and the appearance of treatment limiting anti-drug antibodies. To address these issues, we have developed an innovative ex vivo gene therapy approach, termed transduced autologous restorative gene therapy (TARGT), to produce and secrete adalimumab for the treatment of RA. Helper-dependent (HD) adenovirus vector containing adalimumab light and heavy chain coding sequences was used to transduce microdermal tissues and cells of human and mouse origin ex vivo, rendering sustained secretion of active adalimumab. The genetically engineered tissues were subsequently implanted in a mouse model of RA. Transduced human microdermal tissues implanted in SCID mice demonstrated 49days of secretion of active adalimumab in the blood, at levels of tens of microgram per milliliter. In addition, transduced autologous dermal cells were implanted in the RA mouse model and demonstrated statistically significant amelioration in RA symptoms compared to naïve cell implantation and were similar to recombinant adalimumab bolus injections. The results of the present study report microdermal tissues engineered to secrete active adalimumab as a proof of concept for sustained secretion of antibody from the novel ex vivo gene therapy TARGT platform. This technology may now be applied to a range of antibodies for the therapy of other diseases.

Fully Automated Segmentation of Lung Parenchyma Using Break and Repair Strategy

Abstract The traditional segmentation methods available for pulmonary parenchyma are not accurate because most of the methods exclude nodules or tumors adhering to the lung pleural wall as fat. In this paper, several techniques are exhaustively used in different phases, including two-dimensional (2D) optimal threshold selection and 2D reconstruction for lung parenchyma segmentation. Then, lung parenchyma boundaries are repaired using improved chain code and Bresenham pixel interconnection. The proposed method of segmentation and repairing is fully automated. Here, 21 thoracic computer tomography slices having juxtapleural nodules and 115 lung parenchyma scans are used to verify the robustness and accuracy of the proposed method. Results are compared with the most cited active contour methods. Empirical results show that the proposed fully automated method for segmenting lung parenchyma is more accurate. The proposed method is 100% sensitive to the inclusion of nodules/tumors adhering to the lung pleural wall, the juxtapleural nodule segmentation is >98%, and the lung parenchyma segmentation accuracy is >96%.

Image Retrieval Based on Chain Code Algorithm Using Color and Texture Features

The rapid growth of image retrieval has provided an efficient Content-Based Image Retrieval CBIR system to retrieve image accurately. In this paper, a precise retrieval result by exploiting color, texture and shape features is proposed. First, extract the features by color moment and (Hue, Saturation, Value HSV color space as a color feature, and then get the co-occurrence matrix as well as Discrete Wavelet Transform DWT for a texture feature. Chain codes algorithm, specifically chain code histogram, is then applied to obtain the codes of the shape feature. Second, collect all these features and store it in the database, where each record represents one image of the dataset. Similarity process is executed to find the images that are more similar to the query image, retrieved images ranked. The dataset applied in this study is WANG that includes 10 classes with each class containing 100 images. Experimental results have revealed that the proposed method outperformed the previous studies with an average of 0.824 in term of precision

Improving Pertuzumab production by gene optimization and proper signal peptide selection

Using proper signal peptide and codon optimization are important factors that must be considered when designing the vector to increase protein expression in Chinese Hamster Ovary (CHO) cells. The aim of the present study is to investigate how to enhance Pertuzumab production through heavy and light chain coding gene optimization and proper signal peptide selection. First, CHO-K1 cells were transiently transfected with whole-antibody-gene-optimized, variable-regions-optimized and non-optimized constructs and then we employed five different signal peptides to improve the secretion efficiency of Pertuzumab. Compared to the native antibody gene, a 3.8 fold increase in Pertuzumab production rate was achieved with the whole heavy and light chain sequence optimization. Although an overall two fold increase in monoclonal antibody production was achieved by human albumin signal peptide compared to the control signal peptide, this overproduction was not statistically significant. Selected signal peptides had no effect on the binding of Pertuzumab to the ErbB2 antigen. The combined data indicate that human albumin signal peptide along with whole antibody sequence optimization can be used to improve Pertuzumab production rates. This sequence was used to produce Pertuzumab producing CHO-K1 stably transfected cells. This result is useful for producing Pertuzumab as a biosimilar drug.

DBS(Doppler Beam Sharpening) 영상에서 표적 탐지 방안

DBS(Doppler Beam Sharpening) algorithm is a way to improve azimuth resolution performance in radar. Since DBS image includes the is information about the search area of radar, various clutter components exist besides the target to be detected. To detect and track the desired target in a DBS image, it must be able to identify a target and the clutter components. In this paper, we describe how to use image size and terrain information(DTED) to identify the target in a DBS image. By using morphological filter and chain code, it acquires image size and excludes the clutter components. By matching with DTED, we determine target.

Chain code feature based recognition of handwritten Gujarati numerals

This paper describes chain code based method for handwritten Gujarati numeral recognition. Literature review on Indian OCR indicates that in comparison with Bangla, Hindi, Kannada, Tamil and Telugu scripts, the OCR activities related to Gujarati script is very less. Development of OCR for Gujarati script is quite challenging area for research. In this work, recognition of isolated Gujarati handwritten numerals is performed using chain code based methods. Horizontal scanning and maximum distance from centroid methods are used for deciding the starting point for calculating the chain code sequence. An overall accuracy of 96.37% and 95.62% is obtained using feed forward neural network classifier by the proposed methods respectively. One of the significant contributions of this paper is towards the generation of large and representative database for handwritten Gujarati numerals. Keywords:Chain code, Gujarati handwritten numeral recognition, Neural network classifier.

A 3D stroke based representation of sign language signs using key maximum curvature points and 3D chain codes

Sign Language is a visual spatial language used by deaf and dumb community to convey their thoughts and ideas with the help of hand gestures and facial expressions. This paper proposes a novel 3D stroke based representation of dynamic gestures of Sign Language Signs incorporating local as well as global motion information. The dynamic gesture trajectories are segmented into strokes or sub-units based on Key Maximum Curvature Points (KMCPs) of the trajectory. This new representation has helped us in uniquely representing the signs with fewer number of key frames. We extract 3D global features from global trajectories using a scheme of representing strokes as 3D codes, which involves dividing strokes into smaller units (stroke subsegment vectors or SSVs), and representing them as belonging to one of the 22 partitions. These partitions are obtained using a discretisation procedure which we call an equivolumetric partition (EVP) of sphere. The codes representing the strokes are referred to as an EVP code. In addition to global hand motion and local hand motion, facial expressions are also considered for non-manual signs to interpret the meaning of words completely. In contrast to existing methods, our method of stroke based representation has less expensive training phase since it only requires the training of key stroke features and stroke sequences of each word.

Object Feature Extraction of Songket Image Using Chain Code Algorithm

The study was aimed at determining the feature of a motif found in a Songket image in order to make the object detectable and readable. The method used was image color segmentation in the form of a process of segmentation of the image area based on the similarity in colors, which was continued with the binary process that aims to change the image into binary form (0 and 1), so that it only has two colors namely black and white. This study also used mathematical morphology in detecting objects, by using dilation operation and filling holes. After the process of mathematical morphology was completed, the next process was motif extraction by applying moore contour tracking algorithms and the development of chain code algorithms. The results of the process carried out showed that the development chain code algorithm can generate the number of objects, the length of chain code, and probable value of rate of appearances of each chain code in a motif, despite there are some objects in a motif. Then the values are stored into the database as The Feature of Songket Motifs.

Accurate object recognition in the underwater images using learning algorithms and texture features

Underwater image processing is very challenging due to its environmental conditions and poor sunlight. Images captured from the ocean using autonomous vehicles are often non-uniformly illuminated and contain noise due to the underlying environment. Object recognition is a challenging task under water due to the variation in the environment, target shape and orientation. Traditional algorithms based on spatial information may not lead to accurate segmentation as the intensity variation is often less in underwater images. Texture information representing the characteristics of the object is needed. Statistical features like autocorrelation, sum average, sum variance and sum entropy were extracted. These were fed as input to learning algorithms and training was done to effectively classify the object of interest and background. Chain coding was further applied for object recognition. The proposed methodology achieved a maximum classification accuracy of 96%.

Tortuosidad discreta como medida morfométrica en tumores cerebrales

Morphological changes in brain tumors may be related to their malignancy. The objective of this work is to be able to detect and quantify these changes in a magnetic resonance imaging, since it can represent an important advantage for the noninvasive diagnosis in patients. One way to identify such morphological changes can be through the measurement of their tortuosity. The discrete tortuosity is a descriptor that characterizes bi-dimensional curves, as the contour of a region. In this work an alternative procedure for calculating the volumetric tortuosity of a surface is proposed. This technique is based in the slope chain code of the surface contour of a volume, and here we call it tridimensional discrete tortuosity. This descriptor is used as a morphometric index to study the tortuosity of brain tumors. For this, magnetic resonance images from 20 patients with low and high malignancy levels were analyzed, considering four regions: edema, whole tumor, enhancing region, and necrotic region. As a result, the tortuosities of the different regions are presented, with significant differences only in some of them. It should be noted that a disadvantage that is present, is the dependence of the measurement to the use of a robust method of segmentation, nevertheless the proposal of the discrete tortuosity for volumetric surfaces is satisfactory.

A New Algorithm of Shape Boundaries Based on Chain Coding

A new method to obtain connected component in binary images is presented. The method uses DFA automaton to obtain chain code and label the component boundary. It is theoretically proved that the algorithm improves the image encoding efficiency closer to the lowest time consumption.

Detection of Juxtapleural Nodules in Lung Cancer Cases Using an Optimal Critical Point Selection Algorithm

Detection of lung cancer through image processing is an important tool for diagnosis. In recent years, image processing techniques have become more widely used. Lung segmentation is an essential pre-processing step for most (CAD) schemes. An automated system is proposed in this paper for identifying lung cancer from the analysis of computed tomography images by performing nodule segmentation using an optimal critical point selection algorithm (OCPS) which improves the detection of shape- and size-based juxtapleural nodules located at the lung boundary. A suspect area of nodule is obtained with the help of a bidirectional chain code (BDC) approach and the OCPS This algorithm is used to reduce the time consumption to detect the lung nodule and thereby reduce the computational complexity. Shape and size features are extracted for the area between two critical points to facilitate classification as nodule or non-nodule with the help of a support vector machine and random forest classifiers. This automated method was tested on computed tomography (CT) studies from the lung imaging database consortium (LIDC). The results are compared with the existing techniques using various performance measures such as precision rate, recall rate, accuracy and F-measure. The obtained experimental results indicate that the OCPS combined with a random forest classifier performs better in terms of performance evaluation metrics than existing approaches, with less requirement for computation.

Biometric signature verification system based on freeman chain code and k-nearest neighbor

Signature is one of human biometrics that may change due to some factors, for example age, mood and environment, which means two signatures from a person cannot perfectly matching each other. A Signature Verification System (SVS) is a solution for such situation. The system can be decomposed into three stages: data acquisition and preprocessing, feature extraction and verification. This paper presents techniques for SVS that uses Freeman chain code (FCC) as data representation. Before extracting the features, the raw images will undergo preprocessing stage; binarization, noise removal, cropping and thinning. In the first part of feature extraction stage, the FCC was extracted by using boundary-based style on the largest contiguous part of the signature images. The extracted FCC was divided into four, eight or sixteen equal parts. In the second part of feature extraction, six global features were calculated against split image to test the feature efficiency. Finally, verification utilized Euclidean distance to measured and matched in k-Nearest Neighbors. MCYT bimodal database was used in every stage in the system. Based on the experimental results, the lowest error rate for FRR and FAR were 6.67 % and 12.44 % with AER 9.85 % which is better in term of performance compared to other works using that same database.

FREEMAN CHAIN CODE AS REPRESENTATION IN OFFLINE SIGNATURE VERIFICATION SYSTEM

Over recent years, there has been an explosive growth of interest in the pattern recognition. For example, handwritten signature is one of human biometric that can be used in many areas in terms of access control and security. However, handwritten signature is not a uniform characteristic such as fingerprint, iris or vein. It may change to several factors; mood, environment and age. Signature Verification System (SVS) is a part of pattern recognition that can be a solution for such situation. The system can be decomposed into three stages: data acquisition and preprocessing, feature extraction and verification. This paper presents techniques for SVS that uses Freeman chain code (FCC) as data representation. In the first part of feature extraction stage, the FCC was extracted by using boundary-based style on the largest contiguous part of the signature images. The extracted FCC was divided into four, eight or sixteen equal parts. In the second part of feature extraction, six global features were calculated. Finally, verification utilized k-Nearest Neighbour (k-NN) to test the performance. MCYT bimodal database was used in every stage in the system. Based on our systems, the best result achieved was False Rejection Rate (FRR) 14.67%, False Acceptance Rate (FAR) 15.83% and Equal Error Rate (EER) 0.43% with shortest computation, 7.53 seconds and 47 numbers of features.

Writer Verification Using Spatial Domain Features under Different Ink Width Conditions

In this paper, we present a comparative study of spatial domain features for writer identification and verification with different ink width conditions. The existing methods give high error rates, when comparing two handwritten images with different pen types. To the best of our knowledge, we are the first to design the feature with different ink width conditions. To address this problem, contour based features were extracted using a chain code method. To improve accuracy at higher levels, we considered histograms of chain code and variance in bins of histogram of chain code as features to discriminate handwriting samples. The system was trained and tested for 1,000 writers with two samples using different writing instruments. The feature performance is tested on our newly created dataset of 4,000 samples. The experimental results show that the histogram of chain code feature is good compared to other methods with false acceptance rate of 11.67%, false rejection rate of 36.70%, average error rates of 24.18%, and average verification accuracy of 75.89% on our new dataset. We also studied the effect of amount of text and dataset size on verification accuracy.

Fingerprint reference point identification based on chain encoded discrete curvature and bending energy

Reference point identification is important in automatic fingerprint recognition system as it can be used to align fingerprints in a correct orientation in spite of the possibility of different transformations in fingerprint images. It is also used in fingerprint classification, as it is desirable to classify fingerprint images for forensic type applications which require the input image to be verified against a large database. The important feature information useful for classification is centered near the reference point. Most of the current approaches for identifying the reference point either require determining ridge orientation or use some complex filters. These methods either operate on 2D (two dimensional) or are not robust to rotation or cannot be applied to every class of fingerprint image. This paper proposes a method to reliably identify unique reference point that operates in 1D (one dimensional). The method treats the fingerprint ridges as a non-overlapped sequence of chain code segments. A modified k-curvature method has been proposed to find the high-curvature area of fingerprint ridges. The reference point localization is based on the property of the ridge's bending energy. The proposed method is tested on FVC2002 and FVC2004 standard datasets, and the experimental results show that the proposed algorithm can accurately locate reference point for all types of fingerprint images.

IMPLEMENTASI METODE CHAIN CODE UNTUK PENGENALAN RAMBU LALU LINTAS

Traffic sign is needed to give information to users so they can be aware in roads. There are many types of traffic signs and each has many forms and different from each other so users sometimes have difficuty in recognizing traffic signs. In this research, the signs used are signs based on Peraturan Menteri Perhubungan Republik Indonesia Nomor PM 13 Tahun 2014. Modified Chain Code method was implemented for feature extraction process and Euclidean Distance method is used to calculating the similarity. Testing is done with 5 types of tests i.e. resize image, objects truncated, added a few objects to image, added many objects to image and noise spots. The test results showed the accuracy of the image of traffic signs to be recognized is 92.5%.

Analysis and Recognition of Curve Type as The Basis of Object Recognition in Image

An object in an image when analyzed further will show the characteristics that distinguish one object with another object in an image. Characteristics that are used in object recognition in an image can be a color, shape, pattern, texture and spatial information that can be used to represent objects in the digital image. The method has recently been developed for image feature extraction on objects that share characteristics curve analysis (simple curve) and use the search feature of chain code object. This study will develop an algorithm analysis and the recognition of the type of curve as the basis for object recognition in images, with proposing addition of complex curve characteristics with maximum four branches that will be used for the process of object recognition in images. Definition of complex curve is the curve that has a point of intersection. By using some of the image of the edge detection, the algorithm was able to do the analysis and recognition of complex curve shape well.

A New Dynamic Intelligent Time Domain Arc Furnace Modeling based on Combination Adaptive Neuro-fuzzy Inference System and Chain Code

Electric arc furnaces have intense non-linear, time variant, and uncertain characteristics bringing about voltage and current harmonics, unbalances, and voltage flicker. In this article, a new three-phase dynamic and intelligent arc furnace in time domain based on adaptive neuro-fuzzy inference system is proposed. In this approach, the adaptive neuro-fuzzy inference system is trained by several actual electric arc furnace operations, and a pattern detection based on the modified chain code method is presented. Hence, electric arc furnace performance is analyzed by applying a preprocessing stage using low-pass average filters and chain code method that recognizes the defects in different patterns such as pulses, impulses, similar and inverse profiles, steps, etc. A chain code is a lossless compression algorithm for detecting the behavior of different data. The adaptive neuro-fuzzy inference system is simultaneously trained by electric arc furnace operation at each stage by means of the patterns. For data collection, a power quality analyzer and oscilloscope are connected to different actual electric arc furnaces. Finally, the electric arc furnace model is presented as a voltage source depending on a current similar to a non-linear black box. This model has no linearity in arc characteristic and can show an actual arc.