22 research outputs found
Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets
In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pix- els. We have proposed a novel image enhancement scheme based on intuitionistic hesi- tant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the pho- tographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods.Web of Science500866
Mammography Images Segmentation Based on Fuzzy Set and Thresholding
Breast cancer is the most widespread cancer that influences ladies about the world. Early recognition of breast tumor is a standout amongst the hugest variables influencing the probability of recuperation from the illness. Hence, mammography remains the most precise and best device for distinguishing breast malignancy.
This paper presents a method for segment the boundary of breast masses regions in mammograms via a proposed algorithm based on fuzzy set techniques. Firstly, it was used data set (mini-MIAS) for evaluate algorithm. it was preprocessing the data set to remove noise and propose a fuzzy set by using fuzzy inference system by generated two input parameters (employs image gradient), then used thresholding filter. Then it was evaluated this proposed method, qualitative and quantitative results were obtained to demonstrate the efficiency of this method and confirm the possibility of its use in improving the diagnosis
Enhancement Of The Low Contrast Image Using Fuzzy Set Theory
This paper presents a fuzzy grayscale enhancement technique for low contrast image. The degradation of the low contrast image is mainly caused by the inadequate lighting during image capturing and thus eventually resulted in nonuniform illumination in the image
Wave-Atom and Cycle-Spinning-Based Noise Reduction in Mammography Images
Image denoising is crucial in medical image processing. Digital mammography depends significantly on de-noising for computer-aided-detection of malignant cells like Microcalcifications. In this work, we proposed an unique hybrid approach to reduce Gaussian noise in digital mammograms by combining the wave-atom translation and cycle spinning methods. Pictures denoised by thresholding of coefficients would produce pseudo-Gibbs events because wave atoms are not translationally invariant. Circular motion is applied to keep away the artefacts. Experimental results clearly establish that the method is effective at filtering out background noise while maintaining the integrity of edges and enhancing picture quality. Mini-Mias pictures with variable quantities of Gaussian Noise are used to evaluate and analyse the performance using peak signal-to-noise ratio and structural similarity index. The provided technique outperforms several current filters in terms of evaluated results of peak signal-to-noise ratio and structural similarity index
Quantification and segmentation of breast cancer diagnosis: efficient hardware accelerator approach
The mammography image eccentric area is the breast density percentage
measurement. The technical challenge of quantification in radiology leads to
misinterpretation in screening. Data feedback from society, institutional, and industry
shows that quantification and segmentation frameworks have rapidly become the
primary methodologies for structuring and interpreting mammogram digital images.
Segmentation clustering algorithms have setbacks on overlapping clusters, proportion,
and multidimensional scaling to map and leverage the data. In combination,
mammogram quantification creates a long-standing focus area. The algorithm
proposed must reduce complexity and target data points distributed in iterative, and
boost cluster centroid merged into a single updating process to evade the large storage
requirement. The mammogram database's initial test segment is critical for evaluating
performance and determining the Area Under the Curve (AUC) to alias with medical
policy. In addition, a new image clustering algorithm anticipates the need for largescale
serial and parallel processing. There is no solution on the market, and it is
necessary to implement communication protocols between devices. Exploiting and
targeting utilization hardware tasks will further extend the prospect of improvement in
the cluster. Benchmarking their resources and performance is required. Finally, the
medical imperatives cluster was objectively validated using qualitative and
quantitative inspection. The proposed method should overcome the technical
challenges that radiologists face
Optimisasi Model Fuzzy Terbobot untuk Klasifikasi Data Polikotomus dan Penerapannya di Bidang Kesehatan
Penelitian ini bertujuan untuk mengembangkan metode baru dalam pemodelan fuzzy untuk klasifikasi data polikotomus dengan kombinasi metode aturan fuzzy terbobot (weighted fuzzy rule) dan dekomposisi nilai singular serta mengaplikasikannya untuk mendiagnosis penyakit kanker serviks dan kanker payudara. Target khusus dalam penelitian ini adalah mendapatkan metode baru dalam pemodelan fuzzy terbobot yang optimal untuk klasifikasi data polikotomus, menghasilkan pemrograman graphical user interface (GUI) untuk model fuzzy terbobot yang optimal untuk data polikotomus, dan menerapkannya untuk klasifikasi di bidang kesehatan yaitu untuk diagnosis kanker serviks dan kanker payudara.
Pada penelitian tahun pertama, telah dibangun suatu prosedur baru dalam pembentukan model fuzzy Mamdani yang optimal untuk klasifikasi data polikotomus dengan metode aturan fuzzy terbobot. Kemudian dibangun suatu prosedur baru dalam pembentukan model fuzzy Takagi-Sugeno-Kang (TSK) order satu dengan kombinasi metode aturan fuzzy terbobot dan dekomposisi nilai singular. Berdasarkan prosedur tersebut, dikembangkan pemrograman graphical user interface (GUI) dengan MATLAB untuk klasifikasi data polikotomus. Selanjutnya pada tahun kedua, hasil pada tahun pertama akan diterapkan untuk menyelesaikan permasalahan klasifikasi di bidang kesehatan khususnya untuk diagnosis kanker serviks dan kanker payudara
Optimisasi Model Fuzzy Terbobot untuk Klasifikasi Data Polikotomus dan Penerapannya di Bidang Kesehatan
Penelitian ini bertujuan untuk mengembangkan metode baru dalam pemodelan
fuzzy untuk klasifikasi data polikotomus dengan kombinasi metode aturan fuzzy terbobot
(weighted fuzzy rule) dan dekomposisi nilai singular serta mengaplikasikannya untuk
mendiagnosis penyakit kanker serviks dan kanker payudara. Target khusus dalam
penelitian ini adalah mendapatkan metode baru dalam pemodelan fuzzy terbobot yang
optimal untuk klasifikasi data polikotomus, menghasilkan pemrograman graphical user
interface (GUI) untuk model fuzzy terbobot yang optimal untuk data polikotomus, dan
menerapkannya untuk klasifikasi di bidang kesehatan yaitu untuk diagnosis kanker
serviks dan kanker payudara.
Pada penelitian tahun pertama, telah dibangun suatu prosedur baru dalam
pembentukan model fuzzy Mamdani yang optimal untuk klasifikasi data polikotomus
dengan metode aturan fuzzy terbobot. Kemudian dibangun suatu prosedur baru dalam
pembentukan model fuzzy Takagi-Sugeno-Kang (TSK) order satu dengan kombinasi
metode aturan fuzzy terbobot dan dekomposisi nilai singular. Berdasarkan prosedur
tersebut, dikembangkan pemrograman graphical user interface (GUI) dengan
MATLAB untuk klasifikasi data polikotomus. Selanjutnya pada tahun kedua, hasil pada
tahun pertama akan diterapkan untuk menyelesaikan permasalahan klasifikasi di bidang
kesehatan khususnya untuk diagnosis kanker serviks dan kanker payudara
Empirical approaches to the application of mathematical techniques in health technologies
Mathematical modeling of ageing is built in this paper around research and development activities in cooperation with pharmaceutical companies and hospitals. The interaction of "dirty data" with appropriate mathematical techniques is exemplified mainly with applications to health technologies in endocrinology and oncology. The emphasis is more on old techniques in new situations than on new techniques, though there are references to some novel approaches to modeling
A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms.
Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper's main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic