Discriminative Robust LBP for Leukemia Detection

IJIRST –International Journal for Innovative Research in Science & Technology| Volume 3 | Issue 04 | September 2016 ISSN (online): 2349-6010

Discriminative Robust LBP for Leukemia Detection Nimi T P M. Tech. Scholar Department of Computer Science & Engineering FISAT, Angamaly, Kerala, India

Divya T V Assistant Professor Department of Computer Science & Engineering FISAT, Angamaly, Kerala, India

Abstract Leukemia is a group of cancers that usually begins in the marrow and results in high numbers of abnormal white blood cells. The bone marrow produces the cellular elements of the blood, including platelets, red blood cells and white blood cells. The differences between these groups lie on the texture, color, size and morphology of nucleus and cytoplasm. These white blood cells are not fully developed and are called blasts or leukemia cells. Image processing technique involved five basic components which are image acquisition, image pre-processing, image segmentation, feature extraction and classification. The proposed system firstly individuates in the blood image the leucocytes from the others blood cells, then it selects the lymphocyte cells, it evaluates morphological indexes from those cells and finally it classifies the presence of the leukemia. Discriminative Robust LBP is used for feature extraction. They alleviate the intensity reversal problem of object and background. Our proposed feature is tested on ALL IDB dataset and obtained 89 % accuracy.

Keywords: Segmentation, Thresholding, Leukemia, DRLBP, AML, LBP _______________________________________________________________________________________________________ I.

INTRODUCTION

Leukemia[1] is a cancer of the white blood cells or bone marrow .Bone marrow is a soft tissue present inside the bone. Diagnosing leukemia is based on the fact that white cell count is increased with immature blast (lymphoid or myeloid) cells and decreased neutrophils and platelets. The presence of excess number of blast cells in peripheral blood is a significant symptom of leukemia.

The automatic detection of Leukemia from blood microscopic images generally avoids the problems of manual testing of blood smear and also increases the accuracy. It reduces the computational time and thus increases the efficiency.

The early identification of acute lymphoblastic leukemia symptoms in patients can greatly increase the probability of recovery.

Nowadays the leukemia disease can be identified by automatic specific tests such as Cytogenetics and Immunophenotyping and morphological cell classification made by experienced operators observing blood/marrow microscope images. Those methods are not included into large screening programs and are applied only when typical symptoms appears in normal blood analysis. The Cytogenetics and Immunophenotyping diagnostic methods are currently preferred for their great accuracy with respect to the method of blood cell observation which presents undesirable drawbacks: slowness and it presents a not standardized accuracy since it depends on the operators capabilities and tiredness. Conversely, the morphological analysis just requires an image not a blood sample and hence is suitable for low cost and remote diagnostic systems.

In blood smear, number of red cells is many more than white blood cells. Platelets are small particles and are not clinically important. Blood cells form in the bone marrow, the soft material in the center of most bones. Leukocytes or WBC are cells involved in defending the body against infective organisms and foreign substances. For example, an image may contain up to 100 red cells and only 1 to 3 white cells. Platelets are small particles and are not clinically important. Blood cells form in the bone marrow, the soft material in the center of most bones.

Leukocytes[2] or WBC are cells involved in defending the body against infective organisms and foreign substances. Leukocytes cells containing granules are called granulocytes (composed by neutrophil, basophil, and eosiphil). Cells without granules are called agranulocytes (lymphocyte and monocyte). These cells provide major defense against infections in organisms and their specific concentrations can help specialists to discriminate the presence or the absence of very important families of pathologies.

When infection occurs, the production of WBCs increases.

Leukemia can be pathologically classified into acute and chronic on a broader sense. Acute leukemia is fast-growing and can overrun the body within a few weeks or months. By contrast, chronic leukemia is slow-growing and progressively worsens over years[13]. Acute leukemia involves an overgrowth of very immature blood cells. Chronic leukemia involves an overgrowth of mature blood cells.

ALL is the most common type of leukemia in young children. This disease also affects adults, especially that age 65 and older.

Standard treatments involve chemotherapy and radiotherapy[14]. The survival rates vary by age: 85% in children and 50% in adults. Subtypes include precursor B acute lymphoblastic leukemia, precursor T acute lymphoblastic leukemia, Burkitt's leukemia, and acute biphenotypic leukemia. CLL most often affects adults over the age of 55. It sometimes occurs in younger adults, but it almost never affects children. Two-thirds of affected people are men. The five-year survival rate is 75%. It is incurable, but there are many effective treatments. One subtype is B-cell prolymphocytic leukemia, a more aggressive disease.

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Discriminative Robust LBP for Leukemia Detection (IJIRST/ Volume 3 / Issue 04/ 034)

AML occurs more commonly in adults than in children, and more commonly in men than women. AML is treated with chemotherapy. The five-year survival rate is 40%, except for APL (Acute Promyelocytic Leukemia), which is over 90%. Subtypes of AML, include acute promyelocytic leukemia, acute myeloblastic leukemia, and acute megakaryoblastic leukemia.CML +occurs mainly in adults; a very small number of children also develop this disease. Treatment is with imatinib (Gleevec in United States, Glivec in Europe) or other drugs[12]. The five-year survival rate is 90%.One subtype is chronic myelomonocytic leukemia.

AML is often difficult to diagnose since the precise cause of AML is still unknown. In addition, the symptoms of the disease[3] are very similar to flu or other common diseases, such as fever, weakness, tiredness, or aches in bones or joints .If the described symptoms are present, blood tests, such as a full blood count, renal function and electrolytes, and liver enzyme and blood count, have to be done . Since there is no staging for AML, choosing the type of treatment can vary from chemotherapy, radiation therapy, bone marrow transplant, and biological therapy shows six different images, three depicting healthy cells from non-AML patients and three from AML patients.

II. PROCESS OVERVIEW The first step involves preprocessing the complete images to overcome any background non uniformity due to irregular illumination. Preprocessing also includes color correlation where RGB images are converted to L ∗ a∗ b color space images. This step ensures perceptual uniformity. This step is followed by k-means clustering to bring out the nucleus of each cell. Segmentation is followed by feature extraction based on which classification and validation are performed.

III. PREPROCESSING For AML, we accessed the ALL-IDB for their online image bank of leukemia cells. The the ALL-IDB image bank is a web-based image library that offers comprehensive and growing collections of images relating to a wide range of hematology categories. They provide high-quality images captured using different microscopes in different resolutions.

The images generated by digital microscopes are usually in RGB color space, which is difficult to segment. In practice, the blood cells and image background varies greatly with respect to color and intensity. This can be caused by multiple reasons such as camera settings, varying illumination, and aging stain. In order to make the cell segmentation robust with respect to these variations, an adaptive procedure is used: the RGB input image is converted into the CIELAB or, more correctly, the CIEL ∗ a∗ b∗ color space[11] . The key reasons for these are, first, to reduce memory requirement and to improve the computational time.

Second, the perceptual difference between colors is proportional to the Cartesian distance in the CIELAB color space.

IV. NUCLEI SEGMENTATION The goal of image segmentation[4] is to extract important information from an input image. Segmentation in this system is performed for extracting the nuclei of the leukocytes using color-based clustering. Cluster analysis is the formal study of methods and algorithms for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. k-means is one of the most popular unsupervised learning algorithm and is also a simple clustering algorithm. k-Means Clustering Algorithm: The k-means algorithm requires three user-specified parameters: the number of clusters k, cluster initialization, and distance metric[5]. A k-means clustering procedure is used to assign every pixel to one of the clusters. Every pixel is assigned to one of these classes using the properties of the cluster center. Each pixel of an object is classified into k clusters based on the corresponding ∗a and ∗b values in the L∗ a∗ b color space. Therefore, each pixel in the L ∗a ∗b color space is classified into any of the k clusters by calculating the Euclidean distance between the pixel and each color indicator. These clusters correspond to nucleus (high saturation), background (high luminance and low saturation), and other cells (e. g., erythrocytes and leukocyte cytoplasm).

Each pixel of the entire image will be labeled to a particular color depending on the minimum distance from each indicator[6].

We consider only the cluster that contains the blue nucleus, which is required for the feature extraction. While performing k-means segmentation of complete images, it was observed that, in some of the segmented images, only the edges of the nuclei were obtained as opposed to the whole images of the nuclei[14]. This shortcoming was overcome by employing morphological filtering. The following actions were performed in order to obtain the desired outcome. Once these actions are performed, the following texture and shape-based features are then extracted from these whole images: edge enhancement, canny edge detection, dilation and holefilling.

V. FEATURE EXTRACTION Feature extraction[6] in image processing is a technique of redefining a large set of redundant data into a set of features of reduced dimension. Transforming the input data into the set of features is called feature extraction.

LBP: The concept of local binary pattern (LBP) was introduced for texture classification. For example, the LBP texture features have the following characteristics: 1) They are robust against illumination changes; 2) they are very fast to compute; The LBP method has proved to outperform many existing methods, including the linear discriminant analysis and the principal component

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Discriminative Robust LBP for Leukemia Detection (IJIRST/ Volume 3 / Issue 04/ 034)

analysis. LBP is invariant to monotonic intensity changes. Hence, it is robust to illumination and contrast variations. However, it is sensitive to noise and small pixel value fluctuations.

We give an illustration for how LBP[6] serves as local descriptor. Each neighbor pixel is compared with the center pixel, and the ones whose intensities exceed the center pixel’s are marked as ”1”, otherwise as ”0”. In this way we get a simple circular point features consisting of only binary bits. Typically the feature ring is unfolded as a row vector; and then with a binomial weight assigned to each bit, the row vector is transformed into decimal code for further use.

In this paper, we propose two sets of novel edge-texture features, Discriminative Robust LBP (DRLBP). The proposed features solve the issues of LBP. They alleviate the intensity reversal problem of object and background. Furthermore, DRLBP discriminates local structures that RLBP misrepresent. In addition, the proposed features retain the contrast information of image patterns. They contain both edge and texture information which is desirable for object recognition.

The LBP code at location (x, y) is computed as follows: B 1

LBPx , y 

 s( p

b

 p c ) 2b

b0

1, z  0 s( z)   0, z  0

Where pc is the pixel value at (x, y), pb is the pixel value estimated using bilinear interpolation from neighboring pixels in the b-th location on the circle of radius R around pc and B is the total number of neighboring pixels. A 2B-bin block histogram is computed[9]. There are some patterns that occur more frequently than others and the number of state transitions between 0 and 1 for them is at most two. Such patterns are called uniform patterns and the rest as non uniform. By giving each uniform pattern a bin and collating all non-uniform patterns into a single bin, the bin number is reduced.

The states are changed from 0 to 1 or 1 to 0 during this mapping. By doing so, the code is robust [11]to the reversal in intensity between the background and the objects. We propose mapping a LBP code and its complement to the minimum of the two to differentiate a bright object against a dark background and vice-versa. We name this code as Robust LBP (RLBP). RLBP is computed as follows: RLBP x , y =min {LBP x,y, 2B -1-LBPx,y}, Where LBPx,y is as defined in Eq (1) and 2B −1− LBPx,y is the complement code. RLBP solves the issue of intensity reversal of object and background. It is seen that for both situations, the RLBP feature is the same. To solve the problem of brightness reversal of object and background, RLBP maps all LBP codes to the minimum of the code and its complement.

An object has 2 distinct cues for differentiation from other objects - the object surface texture and the object shape formed by its boundary[6]. The boundary often shows much higher contrast between the object and the background than the surface texture.

Differentiating the boundary from the surface texture brings additional discriminatory information because the boundary contains the shape information. However, in order to be robust to illumination and contrast variations, LBP does not differentiate between a weak contrast local pattern and a strong contrast one. It mainly captures the object texture information[13]. The histogramming of LBP codes only considers the frequencies of the codes i.e. the weight for each code is the same. This makes it difficult to differentiate a weak contrast local pattern and a strong contrast one.

To mitigate this, we propose to fuse edge and texture information in a single representation by modifying the way the codes are histogram med. Instead of considering the code frequencies[7], we assign a weight, ωx,y, to each code which is then voted into the bin that represents the code. The weight we choose is the pixel gradient magnitude which is computed as follows. Following [8], the square root of the pixels is taken. Then, the first order gradients are computed. The gradient magnitude at each pixel is then computed where Ix and Iy are the first-order derivatives in the x and y directions. ωx,y is then used to weigh the LBP code. The stronger the pixel contrast, the larger the weight assigned to the pixel LBP code. In this way, if a LBP code[8] covers both sides of a strong edge, its gradient magnitude will be much larger and by voting this into the bin of the LBP code, we take into account if the pattern in the local area is of a strong contrast[12].Thus, the resulting feature will contain both edge and texture information in a single representation. The value of the i th weighted LBP bin of a M × N block is as follows: hlbp (i)=

M 1

N 1

x0

y0

  ɷx,y δ(LBPx,y ,i), 1, m  n

 (m , n)  

 0 , otherwise

The RLBP histogram is created from the above equation as follows. hrlbp (i) = hlbp (i) + hlbp (2B-1-i), 0< i<2B-1 Where hrlbp(i) is the ith RLBP bin value. consider the absolute difference between the bins of a LBP code and its complement to form Difference of LBP (DLBP) histogram as follows: hdlbp (i) = |hlbp(i) - hlbp (2B -1-i)|, 0< I < 2B-1 th Where hdlbp(i ) is the i DLBP bin value.

The 2 histogram features, RLBP and DLBP, are concatenated to form Discriminative Robust LBP (DRLBP) as follows: hdrlbp (j) = hrlbp (j), 0<j<2B-1 hdlbp(j-2 B-1), 2B-1<j<2B

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Discriminative Robust LBP for Leukemia Detection (IJIRST/ Volume 3 / Issue 04/ 034)

DRLBP contains both edge and texture information. DRLBP represents objects more discriminatively than RLBP. It also resolves the issue of intensity reversal of object and background.

VI. EXPERIMENTAL RESULTS AND ANALYSIS The method was implemented in a MATLAB 2014 prototype and tested with data collected from the ALL IDB Dataset. The dataset consists of images of AML and healthy cells [9]. The image bank is a web-based image library that offers comprehensive and growing collection of images relating to a wide range of hematology categories. They provide high-quality images captured using different microscopes in different resolutions [12]. We are comparing the accuracy of leukemia detection using 2 different features.

To validate the developed method, the computational results obtained by implementing the developed method is evaluated and compared with the methods presented by earlier researchers as well as current practice in the field.

The leukemia detection is demonstrated by an example of images collected from dataset. The example demonstrates the preprocessing [10], nucleus segmentation morphological filtering including dialation, hole filling and feature extraction.

To measure the performance of the proposed method three metrics were used: precision, recall and accuracy. Precision is related to the detection exactness, recall refers to the detection completeness and accuracy refers to the average correctness of the process.

TP stands for True Positive (correctly detected), FP stands for False Positive (incorrectly detected), TN stands for True Negative (correctly not detected),FN stands for False Negative (incorrectly not detected).

Precision=TP/(TP+FP) Recall=TP/(TP+FN) Accuracy= (TP+TN)/(TP+FP+TN+FN) The detection method was first tested for images from the ALL IDB dataset. The test was conducted for LBP and DRLBP features.

Table â&#x20AC;&#x201C; 1 Summary of the results Performance metrics LBP DRLBP Total TP 22 27 Total FP 8 3 Total TN 18 23 Total FN 8 3 Precision 73% 90% Recall 73% 90% Accuracy 71.4% 89.2%

The ROC Characteristics of LBP and DRLBP is shown below. The class labels are identified by taking the maximum value of the match score value. The prediction accuracy of DRLBP is 89 % and LBP is 72%. DRLBP outperforms LBP in all respects.

Fig. 1: ROC Characteristics of LBP

Fig. 2: ROC Characteristics of DRLBP

In fig 2, y- axis represents the true positive rate and x -axis represents the false positive rate. True positive rate measures the proportion of positives that are correctly identified. True negative rate measures the proportion of negatives that are correctly identified. The accuracy obtained is 89%.

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Discriminative Robust LBP for Leukemia Detection (IJIRST/ Volume 3 / Issue 04/ 034)

VII. CONCLUSION The proposed method permits the analysis of blood cells automatically via image processing techniques, and it represents a medical tool to avoid the numerous drawbacks associated with manual observation. Merits and demerits of different methods are derived from experimental results. It is concluded that proposed method is more accurate than other methods. Furthermore, the clinical impact of this research is that it will provide the ability to pathologists to examine a blood smear for finding cancerous cells. This work presents an automated method for detecting leukemia. We propose the novel edge-texture features, Discriminative Robust Local Binary Pattern (DRLBP) for feature extraction. The presented system performs automated processing, including colororrelation, segmentation of the nucleated cells, and effective validation and classification. They alleviate the intensity reversal problem of object and background. Furthermore, DRLBP discriminates local structures more effectively. In addition, the proposed features retain the contrast information of image patterns. They contain both edge and texture information which is desirable for object recognition.

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