Color image segmentation using multi-level thresholding approach and data fusion techniques: application in the breast cancer cells images

Harrabi, Rafika; Braiek, Ezzedine Ben

doi:10.1186/1687-5281-2012-11

Cited by 48 publications

(32 citation statements)

References 28 publications

(68 reference statements)

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“…The first, second, third and fourth SOM with 9, 16, 25 and 36 neurons are set in arrays of 3 × 3, 4 × 4, 5 × 5 and 6 × 6, respectively. The tests are performed using images of the Berkeley Segmentation Database (BSD), which is becoming the benchmark to test algorithms related to color image segmentation (Guo and Sengur, 2013;Harrabi and Braiek, 2012).…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Segmentation of color images by chromaticity features using self-organizing maps

2016

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Usually, the segmentation of color images is performed using cluster-based methods and the RGB space to represent the colors. The drawback with these methods is the a priori knowledge of the number of groups, or colors, in the image; besides, the RGB space is sensitive to the intensity of the colors. Humans can identify different sections within a scene by the chromaticity of its colors of, as this is the feature humans employ to tell them apart. In this paper, we propose to emulate the human perception of color by training a self-organizing map (SOM) with samples of chromaticity of different colors. The image to process is mapped to the HSV space because in this space the chromaticity is decoupled from the intensity, while in the RGB space this is not possible. Our proposal does not require knowing a priori the number of colors within a scene, and non-uniform illumination does not significantly affect the image segmentation. We present experimental results using some images from the Berkeley segmentation database by employing SOMs with different sizes, which are segmented successfully using only chromaticity features.Keywords: Segmentation of color images, color spaces, competitive neural networks. RESUMENUsualmente, la segmentación de imágenes de color se realiza empleando métodos de agrupamiento y el espacio RGB para representar los colores. El problema con los métodos de agrupamiento es que se requiere conocer previamente la cantidad de grupos, o colores, en la imagen; además de que el espacio RGB es sensible a la intensidad de colores. Los humanos podemos identificar diferentes secciones de una escena solo por la cromaticidad de los colores, ya que representa la característica que nos permite diferenciarlos entre sí. En este artículo se propone emular la percepción humana del color al entrenar un mapa auto-organizado (MAO) con muestras de cromaticidad de diferentes colores. La imagen a procesar es transformada al espacio HSV porque en tal espacio la cromaticidad es separada de la intensidad, mientras que en el espacio RGB no es posible. Nuestra propuesta no requiere conocer previamente la cantidad de colores que hay en una escena, y la iluminación no uniforme no afecta significativamente la segmentación de la imagen. Presentamos resultados experimentales utilizando algunas imágenes de la base de segmentación de Berkeley empleando MAOs de diferentes tamaños, las cuales son segmentadas exitosamente empleando únicamente características de cromaticidad.Palabras clave: Segmentación de imágenes de color, espacios de color, redes neuronales competitivas.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…The segmentation of color images has been applied in different areas such as food analysis (Gökmen and Sügüt, 2007;Lopez, Cobos and Aguilera, 2011), geology (Lepistö, Kuntuu and Visa, 2005), medicine (Ghoneim, 2011;Harrabi and Braiek, 2012).…”

Section: Introductionmentioning

confidence: 99%

Segmentation of color images by chromaticity features using self-organizing maps

2016

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“…The major problem of color image segmentation is the high correlation and spatial redundancy of multi-band image histograms and the difficulty of clustering using multi-dimensional histograms. Hence, the general segmentation problem consists in choosing the adopted color model in a specific domain [5] [6]. In the past, many authors have addressed the color image segmentation problems using different methods [7] [13] have proposed a segmentation method based on fuzzy sets and Dempster-Shafer (DS) evidence theory.…”

Section: Imentioning

confidence: 99%

“…It is also possible to convert between color models using the linear and non-linear transformations of the RGB color space. Each color representation has its advantages and disadvantages [4] [5]. There is still no color representation that can dominate the others for all kinds of color images yet.…”

Section: Imentioning

confidence: 99%

Color Image Segmentation Using Fuzzy Clustering and Fusion: Application to Biomedical Images

Chaabane¹

2017

IJRASET

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In this paper, a new color image segmentation method based on Fuzzy clustering and data fusion techniques is resented. The proposed segmentation consists in combining the three component images (R, G and B) of the same image, to gather, in order to increase the information quality and to get an optimal segmented image. In the first step, the segmented images are obtained by applying fuzzy c-means clustering to the information's coming from the three independent component images. In the second step, on the obtained segmented images with specific primitive colors, a combination rule is used to integrate the segmentation results over the three-color components. Experimental investigations and comparative studies with the other previous methods are carried out showing thus the robustness and superiority of the proposed method in terms of medical and textured image segmentation. Index Terms: Segmentation, biomedical image, fuzzy c-means, fuzzy fusion, conflict, and data fusion. I.INTRODUCTION he image segmentation is the process of partitioning an image into homogeneous regions. The goal of segmentation is to locate objects and boundaries in images [1] [2]. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics, such as intensity, color, tone or texture, etc. However, processing images in levels is easy and fast compared to color images. Nevertheless, the color image processing [3] is a very important task when the objects cannot be extracted using gray scale information but can be extracted using color information. The color images processing is one of the important areas. It used in different domains and many techniques have been proposed. Color images can be represented using several color models such as RGB, HSI, YCbCr etc. [3]. It is also possible to convert between color models using the linear and non-linear transformations of the RGB color space. Each color representation has its advantages and disadvantages [4] [5]. There is still no color representation that can dominate the others for all kinds of color images yet. The major problem of color image segmentation is the high correlation and spatial redundancy of multi-band image histograms and the difficulty of clustering using multi-dimensional histograms. Hence, the general segmentation problem consists in choosing the adopted color model in a specific domain [5] [6]. In the past, many authors have addressed the color image segmentation problems using different methods [7] [13] have proposed a segmentation method based on fuzzy sets and Dempster-Shafer (DS) evidence theory. The idea is to assign, at each image pixel level, a mass function that corresponds to a membership function in fuzzy logic. The membership degree of each pixel is determined by applying the FCM algorithm to the gray levels of the image. Then, the DS combination rule and decision are applied to obtain the final segmentation. However, the major problem in the use of Demp...

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“…However, we can cite the fusion model proposed in [4] which merges the individual input segmentations in the within-cluster variance (or inertia) sense (for the set of local label histogram values given by each input segmentations) since, the final segmentation result is optimized by applying a K-means algorithm based fusion scheme. In the same vein, we can also cite the fusion scheme proposed in [48] which uses the same strategy but for the set of local soft labels (computed with a multilevel thresholding scheme) and for which the fusion procedure is thus achieved in the (somewhat) sense of the weighted within-cluster inertia. This fusion of (region-based) segmentation maps can also be achieved in the probabilistic Rand index [49] (PRI) sense, with a consensus function encoding the set of constraints, in terms of pairs of pixel labels (identical or not), provided by each of the segmentations to be combined.…”

Section: : Introductionmentioning

confidence: 99%

A Precision-Recall Criterion Based Consensus Model For Fusing Multiple Segmentations

Mignotte

Hélou

Montréal³

2014

IJSIP

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This paper presents a general framework for seamlessly combining multiple low cost and inaccurate estimated segmentation maps (with an arbitrary number of regions) of the same scene to achieve a final improved segmentation. The proposed fusion model is derived from the well-known precision-recall criterion, specially dedicated to the specific clustering problem of any spatially indexed data and which is also efficient and widely used in the vision community for evaluating both a region-based segmentation and the quality of contours produced by this segmentation map compared to one or multiple ground-truth segmentations of the same image. The proposed combination framework is here specifically designed to be robust with respect to outlier segmentations (that appear to be inconsistent with the remainder of the segmentation ensemble) and includes an explicit internal regularization factor reflecting the inherent ill-posed nature of the segmentation problem. We propose also a hierarchical and efficient way to optimize the consensus energy function related to this fusion model that exploits a simple and deterministic iterative relaxation strategy combining the different segments or individual regions belonging to the segmentation ensemble in the final solution. The experimental results on the Berkeley database with manual ground truth segmentations show the effectiveness of our combination model.

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Color image segmentation using multi-level thresholding approach and data fusion techniques: application in the breast cancer cells images

Cited by 48 publications

References 28 publications

Segmentation of color images by chromaticity features using self-organizing maps

Segmentation of color images by chromaticity features using self-organizing maps

Color Image Segmentation Using Fuzzy Clustering and Fusion: Application to Biomedical Images

A Precision-Recall Criterion Based Consensus Model For Fusing Multiple Segmentations

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