Texture feature based classification on microscopic blood smear for acute lymphoblastic leukemia detection

2019 ◽  
Vol 47 ◽  
pp. 303-311 ◽  
Author(s):  
Sonali Mishra ◽  
Banshidhar Majhi ◽  
Pankaj Kumar Sa
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Blood Smear ◽  
Lymphoblastic Leukemia ◽  
Texture Feature ◽  
Feature Based ◽  
Feature Based Classification

A CLINICOPATHOLOGICAL STUDY OF ACUTE AND CHRONIC LEUKEMIA IN A TERTIARY CARE HOSPITAL

2021 ◽  
pp. 72-74
Author(s):  
Sarat Das ◽  
Prasanta Kr. Baruah ◽  
Sandeep Khakhlari ◽  
Gautam Boro
Keyword(s):  
Acute Myeloid Leukemia ◽  
Acute Lymphoblastic Leukemia ◽  
Peripheral Blood ◽  
Myeloid Leukemia ◽  
Blood Smear ◽  
Lymphoblastic Leukemia ◽  
Peripheral Blood Smear ◽  
Care Hospital ◽  
Cytochemical Study ◽  
Acute Myeloid

Introduction: Leukemias are neoplastic proliferations of haematopoietic stem cells and form a major proportion of haematopoietic neoplasms that are diagnosed worldwide. Typing of leukemia is essential for effective therapy because prognosis and survival rate are different for each type and sub-type Aims: this study was carried out to determine the frequency of acute and chronic leukemias and to evaluate their clinicopathological features. Methods: It was a hospital based cross sectional study of 60 patients carried out in the department of Pathology, JMCH, Assam over a period of one year between February 2018 and January 2019. Diagnosis was based on peripheral blood count, peripheral blood smear and bone marrow examination (as on when available marrow sample) for morphology along with cytochemical study whenever possible. Results: In the present study, commonest leukemia was Acute myeloid leukemia (AML, 50%) followed by Acute lymphoblastic leukemia (ALL 26.6%), chronic myeloid leukemia (CML, 16.7%) and chronic lymphocytic leukemia (CLL, 6.7%). Out of total 60 cases, 36 were male and 24 were female with Male:Female ratio of 1.5:1. Acute lymphoblastic leukemia was the most common type of leukemia in the children and adolescents. Acute Myeloid leukemia was more prevalent in adults. Peripheral blood smear and bone Conclusion: marrow aspiration study still remains the important tool along with cytochemistry, immunophenotyping and cytogenetic study in the diagnosis and management of leukemia.

scholarly journals Acute Lymphoblastic Leukemia Presenting as Fanconi Syndrome

2018 ◽  
Vol 11 (1) ◽  
pp. 63-67
Author(s):  
Tatsunori Yoshida ◽  
Hiroshi Tsujimoto ◽  
Takayuki Ichikawa ◽  
Shinji Kounami ◽  
Hiroyuki Suzuki
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Renal Tubular Acidosis ◽  
Fanconi Syndrome ◽  
Blood Smear ◽  
Lymphoblastic Leukemia ◽  
Peripheral Blood Smear ◽  
Leukemic Cells ◽  
Laboratory Examinations ◽  
Renal Tubular ◽  
Electrolyte Abnormalities

Acute lymphoblastic leukemia (ALL) presenting as Fanconi syndrome (FS) is extremely rare. Here, we report a case of ALL presenting as bilateral nephromegaly following FS. A 2-year-old girl was unexpectedly diagnosed with bilateral nephromegaly. After 2 weeks, she developed general fatigue, thirst, and polyuria. Laboratory examinations revealed renal tubular acidosis, hypokalemia, hypophosphatemia, and aminoaciduria, and FS was diagnosed. Replacement of bicarbonate and potassium did not improve her condition. Two weeks after the onset of FS, leukemic cells appeared on a peripheral blood smear, and the patient was diagnosed with precursor B-cell ALL presenting as nephromegaly and FS. Chemotherapy brought about a prompt resolution of acidosis and electrolyte abnormalities, without renal dysfunction. The patient remains well 4 years after the onset of the disease. Although extremely rare, FS should be recognized as one of the emerging renal complications of ALL.

TAYLOR–MONARCH BUTTERFLY OPTIMIZATION-BASED SUPPORT VECTOR MACHINE FOR ACUTE LYMPHOBLASTIC LEUKEMIA CLASSIFICATION WITH BLOOD SMEAR MICROSCOPIC IMAGES

2021 ◽  
pp. 2150041
Author(s):  
G. MERCY BAI ◽  
P. VENKADESH
Keyword(s):  
Support Vector Machine ◽  
Acute Lymphoblastic Leukemia ◽  
Blood Smear ◽  
Lymphoblastic Leukemia ◽  
Automatic Segmentation ◽  
Texture Features ◽  
Monarch Butterfly ◽  
Support Vector ◽  
Training Time ◽  
Monarch Butterfly Optimization

Acute lymphoblastic leukemia (ALL) is a serious hematological neoplasis that is characterized by the development of immature and abnormal growth of lymphoblasts. However, microscopic examination of bone marrow is the only way to achieve leukemia detection. Various methods are developed for automatic leukemia detection, but these methods are costly and time-consuming. Hence, an effective leukemia detection approach is designed using the proposed Taylor–monarch butterfly optimization-based support vector machine (Taylor–MBO-based SVM). However, the proposed Taylor–MBO is designed by integrating the Taylor series and MBO, respectively. The sparking process is designed to perform the automatic segmentation of blood smear images by estimating optimal threshold values. By extracting the features, such as texture features, statistical, and grid-based features from the segmented smear image, the performance of classification is increased with less training time. The kernel function of SVM is enabled to perform the leukemia classification such that the proposed Taylor–MBO algorithm accomplishes the training process of SVM. However, the proposed Taylor–MBO-based SVM obtained better performance using the metrics, such as accuracy, sensitivity, and specificity, with 94.5751, 95.526, and 94.570%, respectively.

scholarly journals Penggabungan Fitur Tekstur yang Invariant terhadap Iluminasi dan Fitur Bentuk untuk Deteksi Acute Lymphoblastic Leukemia

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Rizal A Saputra ◽  
Chastine Fatichah ◽  
Nanik Suciati
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Local Binary Pattern ◽  
Lymphoblastic Leukemia ◽  
Texture Feature ◽  
Shape Feature ◽  
Feature Extraction Method ◽  
Pattern Shape ◽  
Cell Test ◽  
Lighting Variation ◽  
Better Than

Abstract. Detection with microscopic blood image can help early detection of Accute Lymphoblastic Leukemia (ALL). Therefore, image acquisition process under lighting variation cause varying illumination image, so it’s needed to find texture feature extraction method that is invariant towards illumination. Shape feature also needed in this study because can represent characteristics of microscopic blood image.This study proposes combination of texture feature that is illumination invariant and shape feature for ALL detection. Texture feature will be extracted using Complete Robust Local Binary Pattern (CRLBP) method and will be tested on microscopic blood image dataset named ALL_IDB1. Testing will be conducted by using various combination of different texture feature and shape feature. Combination of shape feature and CRLBP is perform better than others. In indvidual cell test, highest result using SVM Linear with accuracy 90.89%, sensitivity 94.24% and specificity 64.82%. Classification using ALL image reach accuracy 88.00 %, sensitivity 82.35% and specificity 100%.Keywords: Acute Lymphoblastic Leukemia detection, Complete Robust Local Bianry Pattern, Local Binary Pattern, shape feature, texture feature. Abstrak. Deteksi dengan citra mikroskopik sel darah dapat membantu untuk deteksi dini Accute Lymphoblastic Leukemia (ALL). Namun, proses akuisisi citra mikroskopik dengan variasi pencahayaan yang berbeda menyebabkan iluminasi citra menjadi beragam sehingga dibutuhkan metode yang dapat mengekstraksi fitur tekstur yang invariant terhadap iluminasi. Fitur bentuk juga dibutuhkan dalam penelitian ini karena dapat merepresentasikan perbedaan pada citra mikroskopik sel darah. Penelitian ini mengusulkan penggabungan fitur tekstur yang invariant terhadap iluminasi dan fitur bentuk untuk deteksi dini ALL. Fitur tekstur akan diekstraksi dengan menggunakan metode Complete Robust Local Binary Pattern (CRLBP) dan diuji coba pada dataset ALL_IDB1. Uji coba dilakukan dengan variasi penggabungan fitur bentuk dan fitur tekstur. Penggabungan fitur bentuk dan CRLBP merupakan kombinasi fitur dengan performansi paling baik. Pada pengujian sel tunggal memberikan hasil tertinggi pada klasifikasi SVM Linear dengan akurasi 90,89%, sensitifitas 94,24% dan sepesifisitas 64,82%. Pada klasifikasi citra ALL akurasi mencapai 88,00%, dengan sensitifitas 82,35% dan spesifisitas 100%.Kata Kunci: Complete Robust Local Binary Pattern, deteksi Acute Lymphoblastic Leukemia, Local Binary Pattern, fitur bentuk, fitur tekstur

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