Fusion noise-removal technique with modified dark-contrast algorithm for robust segmentation of acute leukemia cell images

Segmentation is the major area of interest in the field of image processing stage. In an automatic diagnosis of acute leukemia disease, the crucial process is to achieve the accurate segmentation of acute leukemia blood image. Generally, there are three requirements of image segmentation for medical purposes, namely; accuracy, robustness and effectiveness which have received considerable critical attention. As such, we propose a new (modified) dark contrast enhancement technique to enhance and automatically segment the acute leukemic cells. Subsequently, we used a fusion 7 × 7 median filter as well as the seeded region growing area extraction (SRGAE) algorithm to minimise the salt-and-pepper noise, apart from preserving the post-segmentation edge. As per the outcomes, the accuracy, sensitivity, and specificity of this method were 91.02%, 83.68%, and 91.57% respectively

Retinal Fundus Image Quality Adjustment with Opponent Color Model

วิทยาศาสตรมหาบัณทิต (วิทยาการคอมพิวเตอร์),2565WHO reported the number of 65 million of Age-related Macular Degeneration (AMD) patients around the world, and expected the number would increase to 300 million patients by the year 2040. Currently, ophthalmologists rely on retinal fundus photographs to analyze AMD lesions. Nevertheless, sometimes the photographs have an unsatisfactory quality such as low contrast, under or over exposure which results in difficulties for the experts to analyze lesions. So, it is suggested to have the unsatisfactory photographs improved to enhance anatomical details appearance before use by the experts. This thesis proposed an effective retinal fundus image simulation modeling to enhance contrast and adjust the color balance. It is aimed to assist ophthalmologists in AMD lesion screening. The proposed method consists of a few steps to achieve the intent. Firstly, an input image is improved contrast with CLAHE technique by using CIE L^* a^* b^* color space. Then, the histogram of the output image from previous step is stretched and rescaled by a scaling histogram technique to adjust its overall brightness offset to meet the Hubbard’s retinal fundus image proper range standard. This thesis used images as experimental data from two datasets, DiaretDB0 and STARE datasets. The results indicate the proposed method yields a highly contrast and color-balance output which fits the Hubbard’s standard and easier to screen lesions.กองทุนวิจัยคณะวิทยาศาสตร์ มหาวิทยาลัยสงขลานครินทร์ สัญญาเลขที่ 2-2561-02-017องค์การอนามัยโลกหรือ WHO ได้รายงานจำนวนผู้ป่วยโรคจุดภาพชัดที่จอตาเสื่อมในผู้สูงอายุ (AMD) จำนวน 65 ล้านรายจากทั่วโลก และตัวเลขจะเพิ่มสูงขึ้นถึง 300 ล้านรายภายในปี ค.ศ. 2040 ในปัจจุบันการวิเคราะห์รอยโรค AMD นั้นใช้การวิเคราะห์ภาพถ่ายสีจอตา ซึ่งบางครั้งภาพถ่ายที่ได้นั้นมีคุณภาพที่ต่ำ เช่น มีคอนทราสต์ที่ต่ำ มีแสงที่มืดหรือสว่างเกินไป เป็นต้น ซึ่งจะทำให้ผู้เชี่ยวชาญวิเคราะห์รอยโรคจากภาพถ่ายนั้นได้ยาก ดังนั้นจึงจำเป็นที่จะต้องทำการปรับปรุงคุณภาพของภาพถ่ายเหล่านี้เพื่อทำให้รายละเอียดทางกายวิภาคดีขึ้นก่อนที่จะให้ผู้เชี่ยวชาญทำการวิเคราะห์รอยโรคต่อไป งานวิจัยชิ้นนี้จึงนำเสนอแบบจำลองเพื่อการปรับปรุงคอนทราสต์และสมดุลสีของภาพถ่ายสีจอตาที่มีประสิทธิภาพ เป้าหมายของงานวิจัยชิ้นนี้เพื่อช่วยผู้เชี่ยวชาญคัดกรองรอยโรค AMD โดยขั้นตอนวิธีของงานวิจัยประกอบด้วย ขั้นตอนแรกจะทำการปรับปรุงคอนทราสต์ของภาพถ่ายด้วยเทคนิค CLAHE โดยใช้แบบจำลองสี CIE L^* a^* b^* และขั้นตอนถัดไปจะทำการยืดและสเกลฮิสโทแกรมของภาพใหม่ด้วยเทคนิคการสเกลฮิสโทแกรมเพื่อปรับปรุงออฟเซตความสว่างโดยรวมของภาพถ่ายให้เป็นตามมาตรฐานช่วงความสว่างของภาพถ่ายสีจอตาที่ดีของ Hubbard งานวิจัยชิ้นนี้ใช้ภาพถ่ายสีจอตาในการทดลองจากสองชุดข้อมูล ได้แก่ ชุดข้อมูล DiaretDB0 และชุดข้อมูล STARE ผลการทดลองแสดงให้เห็นว่าวิธีที่นำเสนอให้ภาพผลลัพธ์ที่มีคอนทราสต์และสมดุลสีที่ดีขึ้นและเหมาะสมสำหรับใช้ในการวิเคราะห์รอยโรคตามมาตรฐานของ Hubbar

Enhancement Citra Fundus Retina Menggunakan CLAHE dan Wiener Filter

Penggunaan gambar fundus retina dalam pendeteksian dan diagnosis awal kelainan atau penyakit pada retina seperti penyakit Diabetic Retinopaty (DR), penyakit kardiovaskular dan penyakit lainnya sekarang ini telah menjadi salah satu bidang yang menarik perhatian bagi para peneliti dan dokter. Tetapi gambar fundus tersebut kadang-kadang memiliki kualitas yang buruk seperti terdapat noise di dalamnya, pencahayaan yang tidak merata serta memiliki kontras yang rendah. Dalam paper ini kami mengusulkan metode untuk meningkatkan kontras dan kualitas gambar fundus serta peningkatan nilai PSNR dari gambar fundus retina asli dengan menggunakan Fast Local Laplacian Filter, Morphology Top-hat Filter, CLAHE dan Wiener Filter.

Modelling on-demand preprocessing framework towards practical approach in clinical analysis of diabetic retinopathy

Diabetic retinopathy (DR) refers to a complication of diabetes and a prime cause of vision loss in middle-aged people. A timely screening and diagnosis process can reduce the risk of blindness. Fundus imaging is mainly preferred in the clinical analysis of DR. However; the raw fundus images are usually subjected to artifacts, noise, low and varied contrast, which is very hard to process by human visual systems and automated systems. In the existing literature, many solutions are given to enhance the fundus image. However, such approaches are particular and limited to a specific objective that cannot address multiple fundus images. This paper has presented an on-demand preprocessing frame work that integrates different techniques to address geometrical issues, random noises, and comprehensive contrast enhancement solutions. The performance of each preprocessing process is evaluated against peak signal-to-noise ratio (PSNR), and brightness is quantified in the enhanced image. The motive of this paper is to offer a flexible approach of preprocessing mechanism that can meet image enhancement needs based on different preprocessing requirements to improve the quality of fundus imaging towards early-stage diabetic retinopathy identification

Low contrast detection factor based contrast enhancement and restoration for underwater images

Marine ecosystem is the largest of earth’s aquatic ecosystems. It includes salt marshes, coral reefs, deep sea, sea floor, etc. To learn deep about the activities taking place inside, underwater imaging is a tool. But these images lack in contrast and brightness leading to the lack of information in the ocean activities. To enhance such low contrast underwater images, Low Contrast Detection Factor (LCDF) is proposed in this study. It uses the value, saturation and hue to enhance the low contrast regions and to restore the color.  Quality assessment is done to substantiate the proposed algorithm. It is found that the entropy gives an average of 7.3. No-reference Quality Metrics such as Natural Image Quality Evaluator and Blind/ Referenceless Image Spatial Quality Evaluator shows an average value of 3.6 and 22.5, respectively. The blur metrics shows a value of 0.21. The quality metrics indicates that the naturalness of the underwater image is maintained while the contrast of the underwater image has increased

Low contrast detection factor based contrast enhancement and restoration for underwater images

7-13Marine ecosystem is the largest of earth’s aquatic ecosystems. It includes salt marshes, coral reefs, deep sea, sea floor, etc. To learn deep about the activities taking place inside, underwater imaging is a tool. But these images lack in contrast and brightness leading to the lack of information in the ocean activities. To enhance such low contrast underwater images, Low Contrast Detection Factor (LCDF) is proposed in this study. It uses the value, saturation and hue to enhance the low contrast regions and to restore the color. Quality assessment is done to substantiate the proposed algorithm. It is found that the entropy gives an average of 7.3. No-reference Quality Metrics such as Natural Image Quality Evaluator and Blind/ Referenceless Image Spatial Quality Evaluator shows an average value of 3.6 and 22.5, respectively. The blur metrics shows a value of 0.21. The quality metrics indicates that the naturalness of the underwater image is maintained while the contrast of the underwater image has increased

Contrast and color balance enhancement for non-uniform illumination retinal images

Color retinal images play an important role in supporting a medical diagnosis. However, some retinal images are unsuitable for diagnosis due to the non-uniform illumination. In order to solve this problem, we propose a method for improving non-uniform illumination that can enhance the image quality of a color fundus photograph suitable for reliable visual diagnosis. Firstly, a hidden anatomical structure in dark regions of the retinal images is revealed by improving the image luminosity with gamma correction. Secondly, multi-scale tone manipulation is then used to adjust the image contrast in the lightness channel of L*a*b* color space. Finally, color balance is adjusted by specifying the image brightness based on Hubbard’s specification. The performance of the applied method has been evaluated against the data from the DIARETDB1 dataset. The results obtained show that the proposed algorithm performs well for correcting the non-uniform illumination of color retinal images

Deep Generative Modeling Based Retinal Image Analysis

In the recent past, deep learning algorithms have been widely used in retinal image analysis (fundus and OCT) to perform tasks like segmentation and classification. But to build robust and highly efficient deep learning models amount of the training images, the quality of the training images is extremely necessary. The quality of an image is also an extremely important factor for the clinical diagnosis of different diseases. The main aim of this thesis is to explore two relatively under-explored area of retinal image analysis, namely, the retinal image quality enhancement and artificial image synthesis. In this thesis, we proposed a series of deep generative modeling based algorithms to perform these above-mentioned tasks. From a mathematical perspective, the generative model is a statistical model of the joint probability distribution between an observable variable and a target variable. The generative adversarial network (GAN), variational auto-encoder(VAE) are some popular generative models. Generative models can be used to generate new samples from a given distribution. The OCT images have inherent speckle noise in it, fundus images do not suffer from noises in general, but the newly developed tele-ophthalmoscope devices produce images with relatively low spatial resolution and blur. Different GAN based algorithms were developed to generate corresponding high-quality images fro its low-quality counterpart. A combination of residual VAE and GAN was implemented to generate artificial retinal fundus images with their corresponding artificial blood vessel segmentation maps. This will not only help to generate new training images as many as needed but also will help to reduce the privacy issue of releasing personal medical data

Procesado de retinografías basado en Deep Learning para la ayuda al diagnóstico de la Retinopatía Diabética

La Retinopatía Diabética (RD) es una complicación de la diabetes y es la causa más frecuente de ceguera en la población laboral activa de los países desarrollados. Sin embargo, cuando se trata de forma precoz, más del 90% de la pérdida de visión se puede prevenir. Las retinografías capturadas durante exámenes oculares regulares son el método estándar para detectar RD. No obstante, el aumento de los casos de diabetes a nivel mundial y la falta de especialistas dificultan el diagnóstico. Las imágenes de fondo de ojo generalmente se obtienen usando cámaras de fondo de ojo en condiciones de luz y ángulos variados. Por lo tanto, estas imágenes son propensas a una iluminación no uniforme, contraste deficiente, bajo brillo y falta de nitidez, lo que provoca imágenes borrosas. Estas imágenes borrosas o con falta de iluminación podrían afectar el diagnóstico clínico. Por lo tanto, mejorar estas imágenes de calidad insuficiente puede ser muy útil para evitar diagnósticos erróneos en sistemas de cribado automáticos o manuales. Recientemente, el aprendizaje automático, especialmente las técnicas basadas en Deep Learning, han supuesto una revolución en el campo de la reconstrucción de imágenes. Por ello, en este trabajo, se propone un método de mejora de calidad de retinografías basado en redes de generativas antagónicas (Generative Adversarial Network, GAN). El modelo está formado por dos redes neuronales convolucionales: una red neuronal que actúa como generador de imágenes sintéticas con el objetivo de engañar a una red discriminadora que está entrenada para distinguir las imágenes generadas de alta calidad de las imágenes reales. Este modelo puede funcionar con imágenes de gran resolución, lo que lo hace ampliamente beneficioso para las imágenes clínicas. En este trabajo, la mejora de calidad de la imagen de fondo de ojo abarca una fase de corrección de la nitidez y una segunda fase de corrección de la iluminación. Para el desarrollo y validación del método propuesto, se utilizó una base de datos propia de 1000 imágenes. Dichas imágenes se dividieron en un conjunto de entrenamiento con 800 imágenes de entrenamiento y un conjunto de test con 200 imágenes, de las cuales la mitad tenían calidad insuficiente para su análisis. Sobre ellas, se aplicó un método con varias etapas. En primer lugar, se abordó la mejora de imágenes borrosas empleando una red profunda de tipo GAN. En segundo lugar, se abordó la mejora de imágenes con falta de iluminación, también a través de una red GAN. Cualitativamente, los resultados obtenidos son satisfactorios. Asimismo, se abordó la evaluación cuantitativa de los resultados desde dos perspectivas: evaluación con referencia y evaluación sin referencia. Para la evaluación sin referencia, se utilizan las métricas Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), Natural Image Quality Evaluator (NIQE) y entropía. En cuanto a la evaluación con una imagen de referencia, se utilizaron la relación señal a ruido (Peak Signal-to-Noise Ratio, PSNR) y el índice de similitud estructural (Structural Similarity Index Measure, SSIM). La evaluación con referencia sirve como guía para comparar las imágenes de buena calidad que han sido degradadas intencionadamente. Por otra parte, la evaluación sin referencia es necesaria para evaluar la mejora que el método produce sobre imágenes de mala calidad ya que, de partida, no se dispone de una versión de buena calidad de dichas imágenes. En la fase de mejora de nitidez y sobre las imágenes de test buena calidad, los resultados obtenidos muestran una mejora del 6.22%, 3.33% y 3.26% en términos de PSNR, SSIM y entropía, respectivamente. No obstante, las medidas BRISQUE y NIQE no presentan una mejora. En esta misma etapa, pero sobre las imágenes de test mala calidad los resultados muestran un 31.80%, 4.27% y 3.89% de mejora en términos de BRISQUE, NIQE y entropía respecto a la imagen original real. Asimismo, en la fase de mejora de imágenes con falta de iluminación, los resultados sobre el conjunto de imágenes de buena calidad muestran una mejora del 156.81%, 14.59%, 3.12% y 2.28% en términos de PSNR, SSIM, BRISQUE y NIQE; mientras que la entropía no presenta una mejoría. En esta fase, y sobre el conjunto de imágenes de mala calidad los resultados reflejan una mejora del 50.62% y un 8.33% en términos de BRISQUE y entropía. Sin embargo, en este grupo de imágenes, la medida NIQE no mejora. Finalmente, se ha llevado a cabo un último experimento con ambas redes en serie. En primer lugar, las imágenes atraviesan la red que corrige la iluminación, y posteriormente se corrige su nitidez con la segunda red. Sobre las imágenes de test de buena calidad se ha conseguido un 4.84%, 5.68%, 3.38% y 2.57% de mejora respecto de la imagen original en términos de PSNR, SSIM, NIQE y entropía, aunque no se observa mejora en términos de BRISQUE. En este último experimento, y sobre las imágenes de test de mala calidad se ha obtenido un 88.95%, 21.17% y 2.46% de mejora en términos de BRISQUE, NIQE y entropía. Los resultados obtenidos muestran que el método propuesto podría ser utilizado como primera etapa dentro de sistemas automáticos de análisis de retinografías para la ayuda al diagnóstico de diversas enfermedades oculares.Diabetic Retinopathy (DR) is a complication of diabetes and the leading cause of blindness worldwide. However, when treated early, more than 90% of vision loss can be prevented. Color fundus photography has been the standard method for detecting DR. However, the growing incidence of diabetes and the lack of specialists make diagnosis difficult. Fundus images are generally obtained using fundus cameras in varied light conditions and angles. Thence, these images are prone to non-uniform illumination, poor contrast, low brightness and lack of sharpness resulting in blurry images. These blurry or poor illuminated images could affect clinical diagnosis. Therefore, improving these poor-quality images can be very helpful in avoiding misdiagnosis in automatic or manual screening systems. Recently, machine learning, especially deep learning techniques, have brought revolution to image super resolution reconstruction. For this reason, in this work, we propose a retinal fundus image enhancement method based on Generative Adversarial Networks (GAN). The model is composed of two convolutional neural networks: a neural network that acts as a generator of synthetic images with the aim of tricking a discriminating network that is trained to distinguish high-quality generated images from real images. This model can work with high resolution images, which makes it widely beneficial for clinical images. In this work, the fundus image enhancement method includes both the sharpness correction and the lighting correction. The proposed technique was evaluated in a proprietary database of 200 images, of which half were of insufficient quality. A method with several stages was applied to them. Firstly, blurry image enhancement was addressed by a GAN network. Secondly, the improvement of images with lack of lighting was addressed, also through a GAN network. To evaluate the retinal image enhancement performance, visual and quantitative evaluation were carried out. Two kinds of image quality assessment were adopted: full-reference and no-reference evaluation. For no-reference assessment, Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), Natural Image Quality Evaluator (NIQE) and Entropy were chosen to assess each enhanced image and its original blurry retinal image. As to full-reference assessment, Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) were used. SSIM and PSNR give the comparison between the enhanced image and the original image. Quantitatively, in the blurred image improvement phase using good quality images, the results obtained show that it is possible to achieve an improvement of 6.22%, 3.33% and 3.26% in terms of PSNR, SSIM and entropy. However, the BRISQUE and NIQE measures do not show an improvement. In this same stage, but on the images of poor quality, the results show a 31.80%, 4.27% and 3.89% improvement in terms of BRISQUE, NIQE and entropy with respect to the real original image. Likewise, in the improvement phase of images with lack of lighting, the results on the set of good quality images show an improvement of 156.81%, 14.59%, 3.12% and 2.28% in terms of PSNR, SSIM, BRISQUE and NIQE; while entropy does not improve. In this phase, using the set of poor-quality images, the results reflect an improvement of 50.62% and 8.33% in terms of BRISQUE and entropy. However, in this group of images the NIQE measure does not improve. Finally, a last experiment was carried out with both networks. First, the images passed through the GAN network that corrected their lighting, and then their sharpness was corrected with the second GAN network. On the good quality test images, the results obtained show an improvement of 4.84%, 5.68%, 3.38% and 2.57% in terms of PSNR, SSIM, NIQE and entropy, although the BRISQUE measure does not improve. In this last experiment, and on the poor-quality test images, the results show an improvement of 88.95%, 21.17% and 2.46% in terms of BRISQUE, NIQE and entropy. The results indicate that the proposed method could be used as a first stage in automatic retinography analysis systems to aid in the diagnosis of various eye diseases.Grado en Ingeniería de Tecnologías de Telecomunicació